diff --git a/static/data/dataset_history.json b/static/data/dataset_history.json index 2eed636..7e99bc0 100644 --- a/static/data/dataset_history.json +++ b/static/data/dataset_history.json @@ -1,6 +1,6 @@ { "_readme": "Hand-maintained going forward — do not regenerate. To add a snapshot, run `python3 scripts/append_dataset_snapshot.py` (counts the live static/data/{datasets,hf_datasets}.json the same way src/lib/datasets.ts computeDatasetStats does: top-level datasets only, iNatAg-mini's images excluded) or append to `points` by hand: { period: \"\", date: \"\", datasetCount: , imageCount: }.", - "generatedAt": "2026-10-01", + "generatedAt": "2026-10-07", "annotations": [ { "atPeriod": "May 30", @@ -225,6 +225,18 @@ "date": "2026-10-01", "datasetCount": 499, "imageCount": 7201467 + }, + { + "period": "Oct 3", + "date": "2026-10-02", + "datasetCount": 583, + "imageCount": 7440150 + }, + { + "period": "Oct 7", + "date": "2026-10-07", + "datasetCount": 666, + "imageCount": 7648711 } ] } diff --git a/static/data/hf_datasets.json b/static/data/hf_datasets.json index f6529af..67bbe82 100644 --- a/static/data/hf_datasets.json +++ b/static/data/hf_datasets.json @@ -3818,7 +3818,7 @@ "sensor_modality": "RGB", "real_or_synthetic": "real", "platform": "handheld", - "environment": "Field", + "environment": "field", "input_data_format": "JPG", "annotation_format": "classLabel", "num_images": 1295, @@ -4312,7 +4312,7 @@ "location": "India", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "uav", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -4346,7 +4346,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4379,7 +4379,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4412,7 +4412,7 @@ "location": "Brazil", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4446,7 +4446,7 @@ "location": "Spain", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4480,7 +4480,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "PNG", "annotation_format": "coco_json", @@ -4584,7 +4584,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4617,7 +4617,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4650,7 +4650,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4683,7 +4683,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4851,7 +4851,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "image", @@ -4885,7 +4885,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "image", @@ -4953,7 +4953,7 @@ "location": "Australia", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -4987,7 +4987,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -5019,7 +5019,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld/ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -5052,7 +5052,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld/ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -5085,7 +5085,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -5118,7 +5118,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "coco_json", @@ -5152,7 +5152,7 @@ "location": "Greece", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -5187,7 +5187,7 @@ "location": "Denmark", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground_fixed", "input_data_format": "PNG", "annotation_format": "directory_names", @@ -5222,7 +5222,7 @@ "location": "Brazil", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Lab", + "environment": "lab", "platform": "uav", "input_data_format": "tif", "annotation_format": "directory_names", @@ -5256,7 +5256,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Lab", + "environment": "lab", "platform": "ground_fixed", "input_data_format": "bmp", "annotation_format": "directory_names", @@ -5290,7 +5290,7 @@ "location": "Pakistan", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -5324,7 +5324,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -5369,7 +5369,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Lab", + "environment": "lab", "platform": "ground", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -5416,7 +5416,7 @@ "location": "Australia", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -5448,7 +5448,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "uav", "input_data_format": "JPG", "annotation_format": "directory_names", @@ -5481,7 +5481,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "PNG", "annotation_format": "directory_names", @@ -5515,7 +5515,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Lab", + "environment": "lab", "platform": "handheld", "input_data_format": "JPEG", "annotation_format": "directory_names", @@ -5548,7 +5548,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld/ground", "input_data_format": "jpg, png, jpeg, JPG", "annotation_format": "directory_names", @@ -5764,7 +5764,7 @@ "location": "Germany", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground_mobile", "input_data_format": "PNG", "annotation_format": "image", @@ -5798,7 +5798,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "JPG", "annotation_format": "image", @@ -5832,7 +5832,7 @@ "location": "United States of America", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "PNG", "annotation_format": "image", @@ -5866,7 +5866,7 @@ "location": "China", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld", "input_data_format": "JPG", "annotation_format": "image", @@ -5900,7 +5900,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "ground", "input_data_format": "PNG", "annotation_format": "image", @@ -5934,7 +5934,7 @@ "location": "Digital, Digital", "sensor_modality": "RGB", "real_or_synthetic": "synthetic", - "environment": "Field", + "environment": "field", "platform": "aerial", "input_data_format": "JPG", "annotation_format": "image", @@ -5960,7 +5960,7 @@ "location": null, "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld/ground", "input_data_format": "JPG", "annotation_format": "image", @@ -5985,7 +5985,7 @@ "location": null, "sensor_modality": null, "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": null, "input_data_format": null, "annotation_format": null, @@ -6020,7 +6020,7 @@ "location": "Greece", "sensor_modality": "RGB", "real_or_synthetic": "real", - "environment": "Field", + "environment": "field", "platform": "handheld/ground", "input_data_format": "JPG", "annotation_format": "image", @@ -9367,7 +9367,7 @@ "machine_learning_task": "image_classification", "agricultural_task": "crop_classification", "location": "Bangladesh", - "environment": "Lab", + "environment": "lab", "real_or_synthetic": "real", "crop_types": [ "Abelmoschus esculentus", @@ -11013,7 +11013,7 @@ "18.442866, 73.884894" ], "country": "India", - "environment": "Mixed", + "environment": "mixed", "real_or_synthetic": "real", "crop_types": [ "banana", @@ -20107,5 +20107,6255 @@ "source": "huggingface", "hf_link": "https://huggingface.co/datasets/Project-AgML/simta_rice_mapping_classification", "examples_image_url": "/img/agml/sample_images/simta_rice_mapping_classification_sample.webp" + }, + { + "name": "pea_seed_viability_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [ + "Beijing, Beijing, China" + ], + "lat_lon": [], + "country": "China", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "pea" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "MV-EM120C CCD camera (Shaanxi Vish Vision Intelligence Manufacturing Technology Co., Ltd., Xi'an, China)" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3000, + "classes": [ + "dead", + "live" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16010081", + "citation": "Sen Men. (2025). *Rapid Seed Viability Detection Using Laser Speckle Weighted Generalized Difference with Improved Residual Networks* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18041269", + "bibtex": "@article{men2026rapid,\n title={Rapid Seed Viability Detection Using Laser Speckle Weighted Generalized Difference with Improved Residual Networks},\n author={Men, Sen and Zhang, Junhao and Liu, Xinhong and Sun, Tianyi and Liu, Wei},\n journal={Agronomy},\n volume={16},\n pages={81},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 270106673, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/pea_seed_viability_classification", + "examples_image_url": "/img/agml/sample_images/pea_seed_viability_classification_sample.webp" + }, + { + "name": "YOLOv8_PlantDisease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "tomato", + "potato", + "pepper" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 20638, + "classes": [ + "Pepper__bell___Bacterial_spot", + "Pepper__bell___healthy", + "Potato___Early_blight", + "Potato___Late_blight", + "Potato___healthy", + "Tomato_Bacterial_spot", + "Tomato_Early_blight", + "Tomato_Late_blight", + "Tomato_Leaf_Mold", + "Tomato_Septoria_leaf_spot", + "Tomato_Spider_mites_Two_spotted_spider_mite", + "Tomato__Target_Spot", + "Tomato__Tomato_YellowLeaf__Curl_Virus", + "Tomato__Tomato_mosaic_virus", + "Tomato_healthy" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0349901", + "citation": "Bytyçi, D., Hasani, Z., Fondaj, J., & Misini, S. (2026). *Dataset, Source Code, and Models for: Deep Learning-based Plant Disease Detection using YOLOv8* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18895667", + "bibtex": "@article{hasani2026comparative,\n title={Comparative evaluation of deep learning models for plant disease classification with edge-aware performance analysis},\n author={Hasani, Zirije and Fondaj, Jakup and Bytyqi, Dionesa and Misini, Sufjan},\n journal={PLOS One},\n volume={21},\n pages={e0349901},\n year={2026},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 344036708, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/YOLOv8_PlantDisease_classification", + "examples_image_url": "/img/agml/sample_images/YOLOv8_PlantDisease_classification_sample.webp" + }, + { + "name": "wetveg2mm_riparian_vegetation_classification", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Jiuzhou River, Guangxi, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Matrice 350 RTK UAV with SHARE 100M A10 medium-format metric camera (Share UAV)" + ], + "collection_period": "October 2025", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 1632, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18152508", + "citation": "Liu, G., Zhang, R., & Qin, H. (2026). *WetVeg-2mm: An Ultra-High-Resolution UAV Orthomosaic Dataset for Fine-Grained Riparian Vegetation Semantic Segmentation* (Version v1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.19849119", + "bibtex": "@article{liu2026wetveg,\n title={WetVeg-2mm: An Ultra-High-Resolution UAV Dataset for Riparian Vegetation Semantic Segmentation},\n author={Liu, Guiqi and Zhang, Runqiao and Qin, Huapeng},\n journal={Remote Sensing},\n volume={18},\n pages={2508},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 1285112160, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/wetveg2mm_riparian_vegetation_classification", + "examples_image_url": "/img/agml/sample_images/wetveg2mm_riparian_vegetation_classification_sample.webp" + }, + { + "name": "LeafNet_plant_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [], + "lat_lon": [], + "country": "", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 4502, + "classes": [ + "diseased", + "healthy" + ], + "license": "cc-by-4.0", + "documentation": "", + "citation": "Siddharth Singh Chouhan. (2022). *A Database of Leaf Images: Practice towards Plant Conservation with Plant Pathology* [Dataset]. Mendeley. https://doi.org/10.17632/HB74YNKJCN.5", + "bibtex": "", + "zip_size_bytes": 7312232877, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/LeafNet_plant_disease_classification", + "examples_image_url": "/img/agml/sample_images/LeafNet_plant_disease_classification_sample.webp" + }, + { + "name": "CT_PestNet_crop_pest_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "ground", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 75222, + "classes": [ + "rice leaf roller", + "rice leaf caterpillar", + "paddy stem maggot", + "asiatic rice borer", + "yellow rice borer", + "rice gall midge", + "Rice Stemfly", + "brown plant hopper", + "white backed plant hopper", + "small brown plant hopper", + "rice water weevil", + "rice leafhopper", + "grain spreader thrips", + "rice shell pest", + "grub", + "mole cricket", + "wireworm", + "white margined moth", + "black cutworm", + "large cutworm", + "yellow cutworm", + "red spider", + "corn borer", + "army worm", + "aphids", + "Potosiabre vitarsis", + "peach borer", + "english grain aphid", + "green bug", + "bird cherry-oataphid", + "wheat blossom midge", + "penthaleus major", + "longlegged spider mite", + "wheat phloeothrips", + "wheat sawfly", + "cerodonta denticornis", + "beet fly", + "flea beetle", + "cabbage army worm", + "beet army worm", + "Beet spot flies", + "meadow moth", + "beet weevil", + "sericaorient alismots chulsky", + "alfalfa weevil", + "flax budworm", + "alfalfa plant bug", + "tarnished plant bug", + "Locustoidea", + "lytta polita", + "legume blister beetle", + "blister beetle", + "therioaphis maculata Buckton", + "odontothrips loti", + "Thrips", + "alfalfa seed chalcid", + "Pieris canidia", + "Apolygus lucorum", + "Limacodidae", + "Viteus vitifoliae", + "Colomerus vitis", + "Brevipoalpus lewisi McGregor", + "oides decempunctata", + "Polyphagotars onemus latus", + "Pseudococcus comstocki Kuwana", + "parathrene regalis", + "Ampelophaga", + "Lycorma delicatula", + "Xylotrechus", + "Cicadella viridis", + "Miridae", + "Trialeurodes vaporariorum", + "Erythroneura apicalis", + "Papilio xuthus", + "Panonchus citri McGregor", + "Phyllocoptes oleiverus ashmead", + "Icerya purchasi Maskell", + "Unaspis yanonensis", + "Ceroplastes rubens", + "Chrysomphalus aonidum", + "Parlatoria zizyphus Lucus", + "Nipaecoccus vastalor", + "Aleurocanthus spiniferus", + "Tetradacus c Bactrocera minax", + "Dacus dorsalis(Hendel)", + "Bactrocera tsuneonis", + "Prodenia litura", + "Adristyrannus", + "Phyllocnistis citrella Stainton", + "Toxoptera citricidus", + "Toxoptera aurantii", + "Aphis citricola Vander Goot", + "Scirtothrips dorsalis Hood", + "Dasineura sp", + "Lawana imitata Melichar", + "Salurnis marginella Guerr", + "Deporaus marginatus Pascoe", + "Chlumetia transversa", + "Mango flat beak leafhopper", + "Rhytidodera bowrinii white", + "Sternochetus frigidus", + "Cicadellidae" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0356061", + "citation": "Cao, W. (2026). *IP102* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.31768954.V1", + "bibtex": "@article{li2026ct,\n title={CT-PestNet: A novel crop pest classification model based on cross-scale feature distillation enhancement and vision transformer with local token interaction},\n author={Li, Youming and Cao, Weilin and Zhang, Liqiang},\n journal={PLOS One},\n volume={21},\n pages={e0356061},\n year={2026},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 3195018300, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/CT_PestNet_crop_pest_classification", + "examples_image_url": "/img/agml/sample_images/CT_PestNet_crop_pest_classification_sample.webp" + }, + { + "name": "mango_pest_original_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [], + "lat_lon": [], + "country": "", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 510, + "augmented_num_images": 46700, + "augmented_zip_size_bytes": 5632146898, + "classes": [ + "apoderus_javanicus", + "aulacaspis_tubercularis", + "ceroplastes_rubens", + "cisaberoptus_kenyae", + "dappula_tertia", + "dialeuropora_decempuncta", + "erosomyia_sp", + "icerya_seychellarum", + "ischnaspis_longirostris", + "mictis_longicornis", + "neomelicharia_sparsa", + "normal", + "orthaga_euadrusalis", + "procontarinia_matteiana", + "procontarinia_rubus", + "valanga_nigricornis" + ], + "license": "cc-by-4.0", + "documentation": "", + "citation": "Chandramouli, K. (2020). *Dataset for pest classification in Mango farms from Indonesia* [Dataset]. Mendeley. https://doi.org/10.17632/94JF97JZC8.1", + "bibtex": "", + "zip_size_bytes": 46647099, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/mango_pest_original_classification", + "examples_image_url": "/img/agml/sample_images/mango_pest_original_classification_sample.webp" + }, + { + "name": "cocoa_pod_disease_gh_image_classification_2", + "machine_learning_task": "object_detection", + "agricultural_task": "disease_classification", + "location": [ + "Wawasi, central region, Ghana" + ], + "lat_lon": [ + "5.617, -1.550" + ], + "country": "Ghana", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cocoa" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon 60D", + "Samsung Galaxy S22" + ], + "collection_period": "20th January, 2023 to March 5th 2024", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 2436, + "classes": [ + "Black Pod Rot", + "Cocoa", + "Pod Borer" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0348147", + "citation": "Techie-Menson, H., Asante, M., Marfo Missah, Y., Abdul-Salaam, G., & Opoku Oppong, S. (2026). *Cocoa Disease Datasets* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.31294003.V1", + "bibtex": "@article{techiemenson2026enhanced,\n title={Enhanced convolutional block attention module with Learnable Gated Fusion (LGF-CBAM) for cocoa pod disease identification},\n author={Techie-Menson, Henry and Asante, Michael and Missah, Yaw Marfo and Abdul-Salaam, Gaddafi and Oppong, Stephen Opoku},\n journal={PLOS One},\n volume={21},\n pages={e0348147},\n year={2026},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 721455655, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/cocoa_pod_disease_gh_image_classification_2", + "examples_image_url": "/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_2_sample.webp" + }, + { + "name": "BerryNet_Lite_strawberry_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "disease_classification", + "location": [ + "Xinxiang County, Henan Province, China" + ], + "lat_lon": [ + "35.231375, 113.895078" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "strawberry" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon camera (Canon Co., LTD. Chaoyang District, Beijing, China)", + "DJIMini3 drone (Shenzhen DJI Innovation Technology Co., LTD., Nanshan District, Shenzhen City, Guangdong Province, China)" + ], + "collection_period": "5 March 2023 to 27 December 2023", + "platform": "drone", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "gpl-3.0", + "documentation": "https://doi.org/10.3390/agriculture14050665", + "citation": "", + "bibtex": "@article{wang2024berrynet,\n title={BerryNet-Lite: A Lightweight Convolutional Neural Network for Strawberry Disease Identification},\n author={Wang, Jianping and Li, Zhiyu and Gao, Guohong and Wang, Yan and Zhao, Chenping and Bai, Haofan and Lv, Yingying and Zhang, Xueyan and Li, Qian},\n journal={Agriculture},\n volume={14},\n pages={665},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 1223812582, + "num_files": 49, + "num_images": 0, + "source": "doi", + "data_link": "https://github.com/wangjianpinghist/Strawberry-Disease-Dataset", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "apple_3d_reconstruction_yield_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Temperate Climate Fruit Growing Experimental Station (EFCT), Embrapa Grape and Wine, Vacaria, Brazil" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "apple" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "ZED X stereo camera (wide-angle lens)", + "RTK-capable GNSS receiver" + ], + "collection_period": "March 2025", + "platform": "mobile", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16070764", + "citation": "Brazilian Agricultural Research Corporation, Teixeira Santos, T., Koenigkan, L., Bharti, D., & Gebler, L. (2025). *SEEmear: high-resolution georeferenced RGB-D data from apple orchard under palmette training system* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17750202", + "bibtex": "@article{bharti2026geo,\n title={Geo-Referenced Factor-Graph SLAM for Orchard-Scale 3D Apple Reconstruction and Yield Estimation},\n author={Bharti, Dheeraj and Faria, Lilian Nogueira de and Koenigkan, Luciano Vieira and Gebler, Luciano and de Rossi, Andrea and Santos, Thiago Teixeira},\n journal={Agriculture},\n volume={16},\n pages={764},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 47782931573, + "num_files": 5, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.17750202", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "soybean_seed_viability", + "machine_learning_task": "unlabeled", + "agricultural_task": "quality_classification", + "location": [ + "Beijing, China" + ], + "lat_lon": [], + "country": "China", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "soybean" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "He–Ne laser (25-LHR-911-230, Melles Griot, Carlsbad, CA, USA; 632.8 nm, 10 mW)", + "CCD camera (MV-EM120C, Microvision, Xi’an, China) equipped with a 16× zoom industrial lens (MVL-KF1624M-25MP, Microvision, Xi’an, China)" + ], + "collection_period": "December 2025", + "platform": "fixed", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16101026", + "citation": "Men. (2026). *Rapid and Non-Destructive Detection of Soybean Seed Viabil-ity Using Temporally Enhanced Speckle Features Coupled with Lightweight Attention Network V2* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20007177", + "bibtex": "@article{men2026rapid,\n title={Rapid and Non-Destructive Detection of Soybean Seed Viability Using Temporally Enhanced Speckle Features Coupled with Lightweight Attention Network},\n author={Men, Sen and Zhang, Junhao and Ni, Lei and Tian, Qinghua and Jin, Xiaoming},\n journal={Agriculture},\n volume={16},\n pages={1026},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 217362308, + "num_files": 1, + "num_images": 3000, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.20007177", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "heliocot_cotton_canopy_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [ + "Yunusemre, Manisa, Türkiye" + ], + "lat_lon": [ + "38.64843595924721, 27.391659701154957" + ], + "country": "Türkiye", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Xiaomi M2010J19SG smartphone camera" + ], + "collection_period": "2025", + "platform": "smartphone", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16111141", + "citation": "Çakaloğulları, U. (2026). *Heliocot dataset: raw and processed field RGB images, annotations, and derived outputs* (Version v1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18243285", + "bibtex": "@article{akaloullar2026heliocot,\n title={Heliocot: A Field RGB Imaging Approach for Diurnal Canopy Orientation Dynamics in Early-Season Cotton},\n author={Çakaloğulları, Uğur and İştipliler, Deniz},\n journal={Agriculture},\n volume={16},\n pages={1141},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 2545232540, + "num_files": 3, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18243285", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "primocane_raspberry_cane", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_phenotyping", + "location": [ + "Lublin Upland macroregion, Poland" + ], + "lat_lon": [ + "51.183, 21.817" + ], + "country": "Poland", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "primocane raspberry" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "DJI Mavic 3 Multispectral (SZ DJI Technology Co., Ltd., Shenzhen, China)" + ], + "collection_period": "2024 and 2025 growing seasons", + "platform": "drone", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "mit", + "documentation": "https://doi.org/10.3390/agriculture16070742", + "citation": "", + "bibtex": "@article{buczyski2026effects,\n title={Effects of Cane Density on Primocane Raspberry Assessed Using UAV-Based Multispectral Imaging},\n author={Buczyński, Kamil and Kapłan, Magdalena and Jarosz, Zbigniew},\n journal={Agriculture},\n volume={16},\n pages={742},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 29535409, + "num_files": 30, + "num_images": 19, + "source": "doi", + "data_link": "https://github.com/kamilczynski/Effects-of-Cane-Density-on-Primocane-Raspberry-Assessed-Using-UAV-Based-Multispectral-Imaging", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "floricane_raspberry_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [ + "Lublin Upland macroregion, Lublin Province, Poland" + ], + "lat_lon": [ + "51.183, 21.817" + ], + "country": "Poland", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "raspberry" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "DJI Mavic 3 Multispectral unmanned aerial vehicle (SZ DJI Technology Co., Ltd., Shenzhen, China)" + ], + "collection_period": "2025", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "mit", + "documentation": "https://doi.org/10.3390/agriculture16171806", + "citation": "", + "bibtex": "@article{buczyski2026impact,\n title={Impact of Biostimulation on Floricane Raspberries Assessed Using Drone-Based Remote Sensing},\n author={Buczyński, Kamil and Kapłan, Magdalena},\n journal={Agriculture},\n volume={16},\n pages={1806},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 10866067, + "num_files": 67, + "num_images": 1, + "source": "doi", + "data_link": "https://github.com/kamilczynski/Impact-of-Biostimulation-on-Floricane-Raspberries-Assessed-Using-Drone-Based-Remote-Sensing", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "maize_lai_rgb_indices", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_phenotyping", + "location": [ + "Pirassununga, SP, Brazil" + ], + "lat_lon": [ + "-21.996, -47.426" + ], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Phantom 4 Pro, SZ DJI Technology Co., Shenzhen, China (20 MP camera)", + "LI-COR Model 3100C, LI-COR, Lincoln, NE, USA" + ], + "collection_period": "29 March to 3 May 2021", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "", + "documentation": "https://doi.org/10.3390/agronomy14040750", + "citation": "", + "bibtex": "@article{demagalhes2024use,\n title={Use of Indices in RGB and Random Forest Regression to Measure the Leaf Area Index in Maize},\n author={de Magalhães, Leonardo Pinto and Rossi, Fabrício},\n journal={Agronomy},\n volume={14},\n pages={750},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 434541, + "num_files": 12, + "num_images": 0, + "source": "doi", + "data_link": "https://osf.io/c2prq/?view_only=2aed5df89ba24821a42cd9f21bfa8162", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "BrazilNutUAVDataset", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Senador Guiomard, Acre, Brazil" + ], + "lat_lon": [ + "-10.067, -67.617" + ], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Brazil nut" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic 3 Pro (DJI, Shenzhen, China)" + ], + "collection_period": "2024", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16030341", + "citation": "Pereira de Carvalho, H., Souza Barros, Q., Linhares Ferreira, E. J., Ferreira, L., Mafra Rodrigues, N. M., Freire da Silva, L., Tabosa de Almeida, B., Gomes Cruz, E., de Mesquita Pinheiro, R., & Pádua, L. (2026). *Brazil Nut Fruit Detection Dataset from UAV Imagery* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18393380", + "bibtex": "@article{pereiradecarvalho2026dataset,\n title={A Dataset for Brazil Nut (Bertholletia excelsa Bonpl.) Fruit Detection in Native Amazonian Forests Using UAV Imagery},\n author={Pereira de Carvalho, Henrique and Barros, Quétila Souza and Ferreira, Evandro José Linhares and Ferreira, Leilson and Rodrigues, Nívea Maria Mafra and Silva, Larissa Freire da and Almeida, Bianca Tabosa de and Gomes Cruz, Erica and Pinheiro, Romário de Mesquita and Pádua, Luís},\n journal={Agronomy},\n volume={16},\n pages={341},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 718426605, + "num_files": 1, + "num_images": 1988, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18393380", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "uav_soil_erosion_assessment_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Alentejo, Portugal" + ], + "lat_lon": [ + "38.3037, -7.2771", + "38.9175, -7.7889", + "38.5175, -7.7381" + ], + "country": "Portugal", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape", + "olive" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Mavic 2 Pro Camera (Hasselblad, Gothenburg, Sweden)" + ], + "collection_period": "2024 to April 2025", + "platform": "drone", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16060645", + "citation": "de Pagter, T., PIJL, A. N. T. O. N., Prats Alegre, S., Coelho, L., Canedo, J. N. G. V., & Nunes, J. P. (2026). *Dataset - Evora 2024-2025 - UAV Surveys - Soil Erosion* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18599366", + "bibtex": "@article{depagter2026uav,\n title={UAV-Based Soil Erosion Assessment in Mediterranean Agricultural Orchards},\n author={de Pagter, Tijs and Canedo, João Nuno Gomes Vicente and Pijl, Anton and Coelho, Luisa and Nunes, João Pedro and Prats, Sergio},\n journal={Agronomy},\n volume={16},\n pages={645},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 4444941255, + "num_files": 1, + "num_images": 44, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18599366", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "strawberry_maturity_detection", + "machine_learning_task": "unlabeled", + "agricultural_task": "maturity_detection", + "location": [ + "Sichuan and Shanxi, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "strawberry" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon EOS 90D" + ], + "collection_period": "2024–2025 winter season", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16111074", + "citation": "L, T. T. (2026). *Strawberry* [Graphic]. Zenodo. https://doi.org/10.5281/ZENODO.18812332", + "bibtex": "@article{leng2026lightweight,\n title={A Lightweight Shape-Aware YOLO Network for Field Strawberry Maturity Detection Under Complex Orchard Conditions},\n author={Leng, Tingting and Zhang, Yuxin and Fan, Yamin and Gao, Huajuan and Zhou, Binhong and Mu, Jiong},\n journal={Agronomy},\n volume={16},\n pages={1074},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 3622393162, + "num_files": 1, + "num_images": 1175, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18812332", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "ghana_crop_parameters_image_classification", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_classification", + "location": [ + "three different agroecological zones across Ghana, Ghana" + ], + "lat_lon": [], + "country": "Ghana", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2020 and 2021", + "platform": "", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-14-5387-2022", + "citation": "Gomez-Dans, J. L., Lewis, P., Yin, F., Asare, K., Lamptey, P., Aidoo, K., MacCarthy, D., Ma, H., Wu, Q., Addi, M., Aboagye-Ntow, S., Doe, C. E., Alhassan, R., Kankam-Boadu, I., Huang, J., & Li, X. (2022). *Location, biophysical and agronomic parameters for croplands in Northern Ghana* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.6632083", + "bibtex": "", + "data_size_bytes": 62858662, + "num_files": 10, + "num_images": 8942, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.6632083", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "worldcereals_crop_mapping_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_classification", + "location": [ + "Global" + ], + "lat_lon": [], + "country": "", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [ + "maize", + "wheat" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2021", + "platform": "", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-15-5491-2023", + "citation": "Lesiv, M., Bilous, A., Bayas, J. C. L., Karanam, S., & Fritz, S. (2023). *Global Crop Type Validation Data Set for ESA WorldCereal System* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7825628", + "bibtex": "", + "data_size_bytes": 3174903, + "num_files": 3, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7825628", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "worldcereals_crop_mapping_classification_2", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_classification", + "location": [ + "Global" + ], + "lat_lon": [], + "country": "", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2021", + "platform": "", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-15-5491-2023", + "citation": "Myroslava Lesiv, Martina Duerauer, Ivelina Georgieva, Andrii Bilous, Juan Carlos Laso Bayas, & Steffen Fritz. (2023). *Global reference data set for validating ESA WorldCereal temporary cropland extent* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7837480", + "bibtex": "", + "data_size_bytes": 186878926, + "num_files": 2, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7837480", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "PL_Forest_CONUS_map_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [], + "lat_lon": [], + "country": "United States", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "2015–2017", + "platform": "", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-16-4619-2024", + "citation": "王 jie. (2024). *30m PALSAR-2/Landsat-based Forest and Evergreen Forest maps in CONUS from 2015 to 2017* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.21270261.V2", + "bibtex": "", + "data_size_bytes": 2493985554, + "num_files": 7, + "num_images": 6, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.21270261.v2", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "plant_image_generation", + "machine_learning_task": "ground_truth", + "agricultural_task": "weed_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "GoPro Hero 7 Black WiFi/Bluetooth controlled", + "EAGL-I (Embedded Automated Generator of Labeled Images) system", + "camera = GoPro and lens = Hero 7 Black" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [ + "Echinochloa crus-galli", + "Cirsium arvense", + "Brassica napus", + "Taraxacum officinale", + "Persicaria spp.", + "Fallopia convolvulus", + "Avena fatua", + "Setaria pumila" + ], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.1371/journal.pone.0243923", + "citation": "Beck, M., Liu, C.-Y., Bidinosti, C., Henry, C., Godee, C., & Ajmani, M. (2020). *Weed seedling images of species common to Manitoba, Canada* (Version 12) [Dataset]. Dryad. https://doi.org/10.5061/DRYAD.GTHT76HHZ", + "bibtex": "@article{beck2020embedded,\n title={An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture},\n author={Beck, Michael A. and Liu, Chen-Yi and Bidinosti, Christopher P. and Henry, Christopher J. and Godee, Cara M. and Ajmani, Manisha},\n journal={PLOS ONE},\n volume={15},\n pages={e0243923},\n year={2020},\n publisher={Public Library of Science}\n}", + "data_size_bytes": 6896022971, + "num_files": 6, + "num_images": 34666, + "source": "doi", + "data_link": "https://doi.org/10.5061/dryad.gtht76hhz", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "duckweed_phenotyping", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_phenotyping", + "location": [ + "N/A" + ], + "lat_lon": [], + "country": "", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "Lemna minor" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Raspberry Pi High Quality Cameras (12.3 MP)", + "Raspberry Pi 4 board", + "Zaber Technologies, LC40B2000 (linear actuator)", + "Opentrons-2 (OT-2) liquid handling system", + "BioPlas, 1 μL Astral inoculation loop, 7000 (modified)" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.1371/journal.pone.0287739", + "citation": "Kose, T., Lins, T., Wang, J., O'Brien, A., Sinton, D., & Frederickson, M. (2023). *Data from: Accelerated high-throughput imaging and phenotyping system for small organisms* (Version 4) [Dataset]. Dryad. https://doi.org/10.5061/DRYAD.T4B8GTJ6T", + "bibtex": "@article{kose2023accelerated,\n title={Accelerated high-throughput imaging and phenotyping system for small organisms},\n author={Kose, Talha and Lins, Tiago F. and Wang, Jessie and O’Brien, Anna M. and Sinton, David and Frederickson, Megan E.},\n journal={PLOS ONE},\n volume={18},\n pages={e0287739},\n year={2023},\n publisher={Public Library of Science}\n}", + "data_size_bytes": 3253696, + "num_files": 2, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5061/dryad.t4b8gtj6t", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sentinel_landcover_semarid_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_classification", + "location": [ + "Mar Menor basin, Murcia Region, Spain" + ], + "lat_lon": [], + "country": "Spain", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "almond", + "olive trees", + "citrus", + "vineyards", + "pepper", + "lettuce", + "artichoke", + "cauliflower", + "broccoli", + "melon", + "watermelon" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1 SAR", + "Sentinel-2 MSI", + "LiDAR" + ], + "collection_period": "2016-2019", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15020312", + "citation": "Gomariz-Castillo, F., & Sarriá, F. A. (2022). *trainPol.zip* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.21231263.V1", + "bibtex": "@article{valdiviesoros2023effect,\n title={Effect of the Synergetic Use of Sentinel-1, Sentinel-2, LiDAR and Derived Data in Land Cover Classification of a Semiarid Mediterranean Area Using Machine Learning Algorithms},\n author={Valdivieso-Ros, Carmen and Alonso-Sarria, Francisco and Gomariz-Castillo, Francisco},\n journal={Remote Sensing},\n volume={15},\n pages={312},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 96838, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.21231263.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "gedi_austrian_alps_vertical_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "National Park “Kalkalpen” forest, Austria" + ], + "lat_lon": [], + "country": "Austria", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "GEDI" + ], + "collection_period": "2019–2020", + "platform": "spaceborne", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15030664", + "citation": "Lippl Florian, Hirschmugl Manuela, & Sobe, C. (2022). *GEDI and ALS LiDAR data for the Upper Austrian national park \"Kalkalpen\"* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7317978", + "bibtex": "@article{hirschmugl2023assessing,\n title={Assessing the Vertical Structure of Forests Using Airborne and Spaceborne LiDAR Data in the Austrian Alps},\n author={Hirschmugl, Manuela and Lippl, Florian and Sobe, Carina},\n journal={Remote Sensing},\n volume={15},\n pages={664},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 24417160, + "num_files": 6, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7317978", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "cover_crop_canopy_height_image_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [ + "Weidenbach, Middle Franconia, Germany" + ], + "lat_lon": [ + "49.2025, 10.6144" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cover crops", + "mustard", + "oil radish", + "winter barley", + "Egyptian clover" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Parrot SEQUOIA+, Paris, France, 1.2-megapixel (MP) sensor mounted on a quadrocopter (Parrot Bluegrass, Paris, France)", + "DJI Mavic Air, Shenzhen, China, gimbal-mounted RGB-Camera with 12.3 MP (DJI FC2103, Shenzhen, China)" + ], + "collection_period": "2019", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "", + "documentation": "https://doi.org/10.3390/rs15061520", + "citation": "Kümmerer, R. (2023). *Kümmerer et al.; Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass; open data* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7713341", + "bibtex": "@article{kmmerer2023high,\n title={Using High-Resolution UAV Imaging to Measure Canopy Height of Diverse Cover Crops and Predict Biomass},\n author={Kümmerer, Robin and Noack, Patrick Ole and Bauer, Bernhard},\n journal={Remote Sensing},\n volume={15},\n pages={1520},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 60522340, + "num_files": 1, + "num_images": 3, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7713341", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "LiDAR_grassland_biomass_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Oostvaardersplassen, Netherlands" + ], + "lat_lon": [], + "country": "Netherlands", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "GeoSLAM ZEB-REVO RT Handheld Mobile Laser Scanner", + "AeroPoints TM (Propeller Aero, Surry Hills, NSW, Australia)" + ], + "collection_period": "2021", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15071754", + "citation": "de Nobel, J., Rijsdijk, K. F., Cornelissen, P., & Seijmonsbergen, A. C. (2023). *Grassland Aboveground Biomass Mapping Dataset* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.22251274.V5", + "bibtex": "@article{denobel2023prediction,\n title={Towards Prediction and Mapping of Grassland Aboveground Biomass Using Handheld LiDAR},\n author={de Nobel, Jeroen S. and Rijsdijk, Kenneth F. and Cornelissen, Perry and Seijmonsbergen, Arie C.},\n journal={Remote Sensing},\n volume={15},\n pages={1754},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 4135561382, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.22251274.v5", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "mangrove_loss_africa_monthly_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Nigeria, Nigeria", + "Guinea-Bissau, Guinea-Bissau", + "Madagascar, Madagascar", + "Mozambique, Mozambique", + "Kenya, Kenya" + ], + "lat_lon": [], + "country": "Nigeria, Guinea-Bissau, Madagascar, Mozambique, Kenya", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2A and -2B satellites (launched in 2015 and 2017)", + "Copernicus Sentinel-1 C-band radar data", + "Planet satellite mosaics (for accuracy assessment)" + ], + "collection_period": "2018 to 2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15082050", + "citation": "Bunting, P., Hilarides, L., Rosenqvist, A., Lucas, R., Kuto, E., Gueye, Y., & Ndiaye, L. (2022). *Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa 2019--2022* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7459306", + "bibtex": "@article{bunting2023global,\n title={Global Mangrove Watch: Monthly Alerts of Mangrove Loss for Africa},\n author={Bunting, Pete and Hilarides, Lammert and Rosenqvist, Ake and Lucas, Richard M. and Kuto, Edmond and Gueye, Yakhya and Ndiaye, Laye},\n journal={Remote Sensing},\n volume={15},\n pages={2050},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 2279385, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7459306", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "hdva_cuban_mangrove_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Caguanes National Park, Cuba" + ], + "lat_lon": [], + "country": "Cuba", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Planetscope and Rapid Eye (<5 m pixels)", + "Sentinel-2 MultiSpectral Instrument (10 m resolution)" + ], + "collection_period": "September 2017 (Hurricane Irma) to 2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15102495", + "citation": "Turner, H. C., & Galford, G. L. (2023). *Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]* (Version 2.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7826011", + "bibtex": "@article{turner2023extent,\n title={Extent, Severity, and Temporal Patterns of Damage to Cuba’s Ecosystems following Hurricane Irma: MODIS and Sentinel-2 Hurricane Disturbance Vegetation Anomaly (HDVA)},\n author={Turner, Hannah C. and Galford, Gillian L. and Hernandez Lopez, Norgis and Falcón Méndez, Armando and Borroto-Escuela, Daily Yanetsy and Hernández Ramos, Idania and González-Díaz, Patricia},\n journal={Remote Sensing},\n volume={15},\n pages={2495},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 409584408, + "num_files": 2, + "num_images": 5, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7826011", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "ECOSO_colombia_land_cover_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_classification", + "location": [ + "Magdalena-Urabá Moist forests, Colombia" + ], + "lat_lon": [], + "country": "Colombia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "palm plantations", + "grassland" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1", + "Sentinel-2" + ], + "collection_period": "2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15102522", + "citation": "Fagua, J., Rodríguez-Buriticá, S., & Jantz, P. (2023). Data availability of the published paper: Advancing High-Resolution Land Cover Mapping in Colombia: The Importance of a Locally Appropriate Legend. *Zenodo*. https://doi.org/10.5281/ZENODO.7799931", + "bibtex": "@article{fagua2023advancing,\n title={Advancing High-Resolution Land Cover Mapping in Colombia: The Importance of a Locally Appropriate Legend},\n author={Fagua, J. Camilo and Rodríguez-Buriticá, Susana and Jantz, Patrick},\n journal={Remote Sensing},\n volume={15},\n pages={2522},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 9403443, + "num_files": 8, + "num_images": 2, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7799931", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "forest_disturbance_time_series", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Europe" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Landsat 5 TM", + "Landsat 7 ETM+", + "Landsat 8 OLI" + ], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41893-020-00609-y", + "citation": "Senf, C. (2020). *Manually interpreted reference data on forest disturbances across Europe* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.4138867", + "bibtex": "@article{senf2020mapping,\n title={Mapping the forest disturbance regimes of Europe},\n author={Senf, Cornelius and Seidl, Rupert},\n journal={Nature Sustainability},\n volume={4},\n pages={63-70},\n year={2020},\n publisher={Springer Science and Business Media LLC}\n}", + "data_size_bytes": 1618015, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.4138867", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "soil_properties_mapping_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Santa Ana Agricultural Research Station, Huancayo province, Peru" + ], + "lat_lon": [ + "-12.0118, -75.2216" + ], + "country": "Peru", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize", + "wheat" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Altum (MicaSense, Seattle, WA, USA) camera", + "DJI Matrice 300 RTK UAV (DJI, Shenzhen, China)" + ], + "collection_period": "2022", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15123203", + "citation": "Loayza, H., Pizarro, S., Cruz, J., & Arbizu, C. (2023). *Dataset for: Digital mapping of agricultural soil properties from high resolution UAV multispectral imagery* [Dataset]. International Potato Center. https://doi.org/10.21223/PKPXQF", + "bibtex": "@article{pizarro2023implementing,\n title={Implementing Cloud Computing for the Digital Mapping of Agricultural Soil Properties from High Resolution UAV Multispectral Imagery},\n author={Pizarro, Samuel and Pricope, Narcisa G. and Figueroa, Deyanira and Carbajal, Carlos and Quispe, Miriam and Vera, Jesús and Alejandro, Lidiana and Achallma, Lino and Gonzalez, Izamar and Salazar, Wilian and Loayza, Hildo and Cruz, Juancarlos and Arbizu, Carlos I.},\n journal={Remote Sensing},\n volume={15},\n pages={3203},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 1625491132, + "num_files": 5, + "num_images": 2, + "source": "doi", + "data_link": "https://doi.org/10.21223/PKPXQF", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "fmc_10h_training_conus", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "CONUS", + "United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "VIIRS instrument on board Suomi-NPP satellite system" + ], + "collection_period": "2019–2021", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15133372", + "citation": "Schreck, J. S., Petzke, W., Jimenez, P. A., Brummet, T., Knievel, J. C., James, E., Kosovic, B., & Gagne, D. J. (2023). *Datasets used in \"Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management\"* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8030683", + "bibtex": "@article{schreck2023machine,\n title={Machine Learning and VIIRS Satellite Retrievals for Skillful Fuel Moisture Content Monitoring in Wildfire Management},\n author={Schreck, John S. and Petzke, William and Jiménez, Pedro A. and Brummet, Thomas and Knievel, Jason C. and James, Eric and Kosović, Branko and Gagne, David John},\n journal={Remote Sensing},\n volume={15},\n pages={3372},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 3441896267, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.8030683", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sentinel2_crop_mapping", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_classification", + "location": [ + "Danubian Lowlands, Slovakia", + "eastern Slovakian Lowlands, Slovakia" + ], + "lat_lon": [], + "country": "Slovakia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rapeseed", + "barley", + "wheat", + "sunflower", + "corn", + "sugar beet", + "soybean", + "permanent grasslands" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2A and Sentinel-2B satellites" + ], + "collection_period": "2018–2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15133414", + "citation": "Tomáš Rusňák, & Halabuk, A. (2023). Reference data for article: Crop Mapping Without labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-year Sentinel-2 Series and Machine Learning. *Zenodo*. https://doi.org/10.5281/ZENODO.7912749", + "bibtex": "@article{rusk2023crop,\n title={Crop Mapping without Labels: Investigating Temporal and Spatial Transferability of Crop Classification Models Using a 5-Year Sentinel-2 Series and Machine Learning},\n author={Rusňák, Tomáš and Kasanický, Tomáš and Malík, Peter and Mojžiš, Ján and Zelenka, Ján and Sviček, Michal and Abrahám, Dominik and Halabuk, Andrej},\n journal={Remote Sensing},\n volume={15},\n pages={3414},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 27924940, + "num_files": 2, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7912749", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "lidar_agb_uncertainty_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "North American ecoregions, North America" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "Optech Gemini ALS sensor (NEON Airborne Observation Platform)", + "Airborne Laser Scanning (ALS) data" + ], + "collection_period": "2018", + "platform": "aerial", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs15143509", + "citation": "Cushman, Katherine (K. C.). (2023). *Replication code and data “Small field plots can cause substantial uncertainty in gridded aboveground biomass products from airborne lidar data”* [Dataset]. Root. https://doi.org/10.48577/JPL.FIAYOG", + "bibtex": "@article{cushman2023small,\n title={Small Field Plots Can Cause Substantial Uncertainty in Gridded Aboveground Biomass Products from Airborne Lidar Data},\n author={Cushman, K. C. and Saatchi, Sassan and McRoberts, Ronald E. and Anderson-Teixeira, Kristina J. and Bourg, Norman A. and Chapman, Bruce and McMahon, Sean M. and Mulverhill, Christopher},\n journal={Remote Sensing},\n volume={15},\n pages={3509},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 462601468, + "num_files": 1, + "num_images": 830, + "source": "doi", + "data_link": "https://doi.org/10.48577/jpl.FIAYOG", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "molca_land_cover_africa_amazon_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "land_use_classification", + "location": [ + "Amazon, Brazil", + "Siberia, Russia", + "Sub-Saharan Africa" + ], + "lat_lon": [], + "country": "Brazil, Russia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2", + "GL30" + ], + "collection_period": "2016–2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15153774", + "citation": "Bratic, G., & Brovelli, M. A. (2023). *Map Of Land Cover Agreement - MOLCA* (Version v1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8071675", + "bibtex": "@article{bratic2023map,\n title={Map of Land Cover Agreement: Ensambling Existing Datasets for Large-Scale Training Data Provision},\n author={Bratic, Gorica and Oxoli, Daniele and Brovelli, Maria Antonia},\n journal={Remote Sensing},\n volume={15},\n pages={3774},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 7665027861, + "num_files": 2077, + "num_images": 2075, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.8071675", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "cropland_5km_northeast_china_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_classification", + "location": [ + "mainland China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Landsat Thematic Mapper (TM)", + "Enhanced Thematic Mapper Plus (ETM+)", + "ESA Sentinel-2", + "Google Earth Engine (GEE)" + ], + "collection_period": "1900–2016", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-13-3203-2021", + "citation": "Yu, Z., Xiaobin Jin, Lijuan Miao, & Xuhong Yang. (2021). *ChinaCropland.zip （V2）* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.13356680.V2", + "bibtex": "@article{yu2021historical,\n title={A historical reconstruction of cropland in China from 1900 to 2016},\n author={Yu, Zhen and Jin, Xiaobin and Miao, Lijuan and Yang, Xuhong},\n journal={Earth System Science Data},\n volume={13},\n pages={3203-3218},\n year={2021},\n publisher={Copernicus GmbH}\n}", + "data_size_bytes": 56394368, + "num_files": 1, + "num_images": 117, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.13356680.v2", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "olive_stress_hyperspectral_gr_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "disease_detection", + "location": [ + "Halkidiki, Northern Greece, Greece" + ], + "lat_lon": [], + "country": "Greece", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "olive trees" + ], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "Cubert S185 (CUBERT-GmbH, Germany) hyperspectral imager" + ], + "collection_period": "2021", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15245683", + "citation": "Navrozidis, I. (2023). *Ground truth and raw hyperspectral files of olive trees for plant stress detection* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10336545", + "bibtex": "@article{navrozidis2023application,\n title={Application of Machine Learning for Disease Detection Tasks in Olive Trees Using Hyperspectral Data},\n author={Navrozidis, Ioannis and Pantazi, Xanthoula Eirini and Lagopodi, Anastasia and Bochtis, Dionysios and Alexandridis, Thomas K.},\n journal={Remote Sensing},\n volume={15},\n pages={5683},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 7495462, + "num_files": 9, + "num_images": 8, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10336545", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "mangrove_sundarbans_change_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Sundarbans, India/Bangladesh" + ], + "lat_lon": [], + "country": "India, Bangladesh", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Landsat-8 OLI" + ], + "collection_period": "1988 to 2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "gpl-3.0+", + "documentation": "https://doi.org/10.3390/rs15030625", + "citation": "Zhang, Z. (2022). *Annual mangrove maps from 1988 to 2022 in the Sundarbans* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.21779636.V1", + "bibtex": "@article{zhang2023monitoring,\n title={Monitoring of 35-Year Mangrove Wetland Change Dynamics and Agents in the Sundarbans Using Temporal Consistency Checking},\n author={Zhang, Zhen and Ahmed, Md Rasel and Zhang, Qian and Li, Yi and Li, Yangfan},\n journal={Remote Sensing},\n volume={15},\n pages={625},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 31814489, + "num_files": 3, + "num_images": 40, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.21779636.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "GLCFCS30_land_cover_mapping", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_classification", + "location": [ + "Global" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Landsat 8 (OLI: 9 bands, 30 m resolution; TIRS: 2 bands, 100 m resolution)", + "ASTER Global Digital Elevation Model (GDEM) (30 m resolution)" + ], + "collection_period": "2015", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-13-2753-2021", + "citation": "Liangyun, L., Xiao, Z., Xidong, C., Yuan, G., & Jun, M. (2020). *GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery* (Version v1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.3986872", + "bibtex": "@article{zhang2021glc,\n title={GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery},\n author={Zhang, Xiao and Liu, Liangyun and Chen, Xidong and Gao, Yuan and Xie, Shuai and Mi, Jun},\n journal={Earth System Science Data},\n volume={13},\n pages={2753-2776},\n year={2021},\n publisher={Copernicus GmbH}\n}", + "data_size_bytes": 26871940564, + "num_files": 948, + "num_images": 947, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.3986872", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "amazon_blowdown_recovery_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Barcelos, Amazonas state, Brazil", + "southeast Colombia", + "northeast Mato Grosso, Brazil" + ], + "lat_lon": [ + "-0.03, -61.16", + "-1.35, -69.96", + "-10.67, -53.63" + ], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Landsat-8 OLI", + "PlanetScope NICFI" + ], + "collection_period": "September 2020 to May 2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15123196", + "citation": "Dazhou Ping. (2022). *Amazon Blowdowns Disturbance Output*. Zenodo. https://doi.org/10.5281/ZENODO.7036248", + "bibtex": "@article{ping2023assessing,\n title={Assessing the Magnitude of the Amazonian Forest Blowdowns and Post-Disturbance Recovery Using Landsat-8 and Time Series of PlanetScope Satellite Constellation Data},\n author={Ping, Dazhou and Dalagnol, Ricardo and Galvão, Lênio Soares and Nelson, Bruce and Wagner, Fabien and Schultz, David M. and Bispo, Polyanna da C.},\n journal={Remote Sensing},\n volume={15},\n pages={3196},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 55559, + "num_files": 2, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7036248", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "ALandCover_global_discrete_and", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_classification", + "location": [ + "Global" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Landsat Thematic Mapper (TM)", + "Enhanced Thematic Mapper Plus (ETM+)", + "ESA Sentinel-2", + "Google Earth Engine (GEE)" + ], + "collection_period": "2015", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs12061044", + "citation": "Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E., Herold, M., & Fritz, S. (2020). *Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2015: Globe* (Version V3.0.1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.3939038", + "bibtex": "@article{buchhorn2020copernicus,\n title={Copernicus Global Land Cover Layers—Collection 2},\n author={Buchhorn, Marcel and Lesiv, Myroslava and Tsendbazar, Nandin-Erdene and Herold, Martin and Bertels, Luc and Smets, Bruno},\n journal={Remote Sensing},\n volume={12},\n pages={1044},\n year={2020},\n publisher={MDPI AG}\n}", + "data_size_bytes": 39256983013, + "num_files": 15, + "num_images": 15, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.3939038", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tree_species_hyperspectral_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Teakettle Experimental Forest, Fresno County, California, USA" + ], + "lat_lon": [ + "36.9975, -119.0244" + ], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Pinus lambertiana", + "Quercus kelloggii", + "Abies magnifica", + "Abies concolor", + "Pinus jeffreyi", + "Pinus contorta" + ], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "airborne remote sensing platform AOP", + "independently integrated UAV hyperspectral system of the Chinese Academy of Surveying and Mapping" + ], + "collection_period": "2017", + "platform": "airborne", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs11192326", + "citation": "Fricker, G. A., Ventura, J. D., Wolf, J., North, M. P., Davis, F. W., & Franklin, J. (2019). *A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.3470250", + "bibtex": "@article{fricker2019convolutional,\n title={A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery},\n author={Fricker, Geoffrey A. and Ventura, Jonathan D. and Wolf, Jeffrey A. and North, Malcolm P. and Davis, Frank W. and Franklin, Janet},\n journal={Remote Sensing},\n volume={11},\n pages={2326},\n year={2019},\n publisher={MDPI AG}\n}", + "data_size_bytes": 18913753057, + "num_files": 23, + "num_images": 6, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.3470250", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sentinel2_forest_loss_malaysia_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Terengganu and Kelantan, Peninsular Malaysia" + ], + "lat_lon": [], + "country": "Malaysia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2017 and 2018", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs16020284", + "citation": "", + "bibtex": "@article{louis2024tiger,\n title={Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST},\n author={Louis, Valentin and Page, Susan E. and Tansey, Kevin J. and Jones, Laurence and Bika, Konstantina and Balzter, Heiko},\n journal={Remote Sensing},\n volume={16},\n pages={284},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 2302524, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.14473374.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "InVEST_HQI_forest_loss_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Terengganu and Kelantan, Peninsular Malaysia" + ], + "lat_lon": [], + "country": "Malaysia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2017 and 2018", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs16020284", + "citation": "", + "bibtex": "@article{louis2024tiger,\n title={Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST},\n author={Louis, Valentin and Page, Susan E. and Tansey, Kevin J. and Jones, Laurence and Bika, Konstantina and Balzter, Heiko},\n journal={Remote Sensing},\n volume={16},\n pages={284},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 94378646, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.14473398.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "InVEST_HQI_tiger_habitat_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Terengganu and Kelantan, Peninsular Malaysia" + ], + "lat_lon": [], + "country": "Malaysia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2017 and 2018", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs16020284", + "citation": "", + "bibtex": "@article{louis2024tiger,\n title={Tiger Habitat Quality Modelling in Malaysia with Sentinel-2 and InVEST},\n author={Louis, Valentin and Page, Susan E. and Tansey, Kevin J. and Jones, Laurence and Bika, Konstantina and Balzter, Heiko},\n journal={Remote Sensing},\n volume={16},\n pages={284},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 94378646, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.14473401.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sentinel1_rio_negro_drought_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Rio Negro River basin, Brazil" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1 satellite radar images" + ], + "collection_period": "2022 and 2023", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16061056", + "citation": "Wagner, F. H. (2024). *Water surface occurrence and recurrence from the article \"Amazon's 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction\"* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10552960", + "bibtex": "@article{wagner2024amazon,\n title={The Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction},\n author={Wagner, Fabien H. and Favrichon, Samuel and Dalagnol, Ricardo and Hirye, Mayumi C. M. and Mullissa, Adugna and Saatchi, Sassan},\n journal={Remote Sensing},\n volume={16},\n pages={1056},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 166708532, + "num_files": 2, + "num_images": 2, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10552960", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "uav_thermal_habitat_assessment_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Straightwater wildlife management area (WMA), Nebraska, USA", + "Smith waterfowl production area (WPA), Nebraska, USA", + "Johnson WMA, Nebraska, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "thermal", + "imaging_equipment": [ + "FLIR Duo Pro R", + "DJI Matrice 600 Pro" + ], + "collection_period": "2018–2019", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16061081", + "citation": "Hu, Q. (2024). *UAV-derived waterfowl thermal imagery dataset* [Dataset]. Mendeley Data. https://doi.org/10.17632/46K66MZ9SZ.5", + "bibtex": "@article{hu2024combining,\n title={Combining Multi-View UAV Photogrammetry, Thermal Imaging, and Computer Vision Can Derive Cost-Effective Ecological Indicators for Habitat Assessment},\n author={Hu, Qiao and Zhang, Ligang and Drahota, Jeff and Woldt, Wayne and Varner, Dana and Bishop, Andy and LaGrange, Ted and Neale, Christopher M. U. and Tang, Zhenghong},\n journal={Remote Sensing},\n volume={16},\n pages={1081},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 579828241, + "num_files": 2888, + "num_images": 2847, + "source": "doi", + "data_link": "https://doi.org/10.17632/46k66mz9sz.5", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "NEALGEBA_fire_history_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "NE Algeria, Algeria" + ], + "lat_lon": [ + "36.21, 3.71", + "37.08, 8.68" + ], + "country": "Algeria", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Landsat 1–5 MSS (1972–1999)", + "Landsat-4 TM (1982–1993)", + "Landsat-5 TM (1984–2012)", + "Landsat-7 ETM + (1999–2021)", + "Landsat-8 OLI/TIRS (2013–present day)", + "Landsat-9 OLI-2/TIRS-2 (2021–present day)", + "Sentinel-2A and 2B (July 2015 and July 2017, respectively)" + ], + "collection_period": "1984–2023", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16132500", + "citation": "Kouachi, M. E., Khairoun, A., Baeza, M. J., & Moutahir, H. (2024). *Forty-year fire history reconstruction from Landsat data in Mediterranean ecosystems of Algeria (1984–2023)* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10684711", + "bibtex": "@article{kouachi2024forty,\n title={Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards},\n author={Kouachi, Mostefa E. and Khairoun, Amin and Moghli, Aymen and Rahmani, Souad and Mouillot, Florent and Baeza, M. Jaime and Moutahir, Hassane},\n journal={Remote Sensing},\n volume={16},\n pages={2500},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 62220643, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10684711", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "africa_soil_total_n", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Africa" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "", + "imaging_equipment": [ + "PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral imagery" + ], + "collection_period": "2001–2017", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41598-021-85639-y", + "citation": "Hengl, T., Miller, M., Križan, J., Kilibarda, M., Acquah, G., & Sila, A. M. (2020). *iSDAsoil: soil total organic Nitrogen for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths* (Version v0.13) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.4090386", + "bibtex": "@article{hengl2021african,\n title={African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning},\n author={Hengl, Tomislav and Miller, Matthew A. E. and Križan, Josip and Shepherd, Keith D. and Sila, Andrew and Kilibarda, Milan and Antonijević, Ognjen and Glušica, Luka and Dobermann, Achim and Haefele, Stephan M. and McGrath, Steve P. and Acquah, Gifty E. and Collinson, Jamie and Parente, Leandro and Sheykhmousa, Mohammadreza and Saito, Kazuki and Johnson, Jean-Martial and Chamberlin, Jordan and Silatsa, Francis B. T. and Yemefack, Martin and Wendt, John and MacMillan, Robert A. and Wheeler, Ichsani and Crouch, Jonathan},\n journal={Scientific Reports},\n volume={11},\n pages={6130},\n year={2021},\n publisher={Springer Science and Business Media LLC}\n}", + "data_size_bytes": 45408567904, + "num_files": 5, + "num_images": 4, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.4090386", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "LesivPts_ECR_radius", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Northeast China Plain, China", + "North China Plain, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "spring wheat", + "corn", + "soybeans", + "sorghum", + "rice", + "winter wheat", + "peanuts", + "cotton", + "vegetables", + "fruits" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2 A/B satellites" + ], + "collection_period": "2019", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16162981", + "citation": "Suchen, X. (2024). *farmland size estimation in smallholder agriculture using edge counting and remote sensing* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.26339845.V1", + "bibtex": "@article{du2024novel,\n title={A Novel Approach for Farmland Size Estimation in Small-Scale Agriculture Using Edge Counting and Remote Sensing},\n author={Du, Jingnan and Xu, Sucheng and Li, Jinshan and Duan, Jiakun and Xiao, Wu},\n journal={Remote Sensing},\n volume={16},\n pages={2981},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 114246, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.26339845.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "unmanaged_forest_carbon_stock_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Dürrenstein-Lassingtal, Lower Austria, Austria" + ], + "lat_lon": [], + "country": "Austria", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic Mini (SZ DJI Technology Co., Shenzhen, China), 12 Megapixel RGB sensor" + ], + "collection_period": "2023", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16213926", + "citation": "Leditznig, T., & Klug, H. (2024). *Estimating carbon stock in unmanaged forests using field data and remote sensing* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.11657557", + "bibtex": "@article{leditznig2024estimating,\n title={Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing},\n author={Leditznig, Thomas and Klug, Hermann},\n journal={Remote Sensing},\n volume={16},\n pages={3926},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 305471109, + "num_files": 1, + "num_images": 2, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.11657557", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "terraceDL_geomorphic_feature_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_segmentation", + "location": [ + "Iowa, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "United States Geological Survey’s 3D Elevation Program (3DEP)" + ], + "collection_period": "", + "platform": "aerial", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1029/2023ea002845", + "citation": "Maxwell, A. (2023). *terraceDL: A geomorphology deep learning dataset of agricultural terraces in Iowa, USA* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.22320373.V2", + "bibtex": "@article{maxwell2023exploring,\n title={Exploring the Influence of Input Feature Space on CNN‐Based Geomorphic Feature Extraction From Digital Terrain Data},\n author={Maxwell, Aaron E. and Odom, William E. and Shobe, Charles M. and Doctor, Daniel H. and Bester, Michelle S. and Ore, Tobi},\n journal={Earth and Space Science},\n volume={10},\n pages={e2023EA002845},\n year={2023},\n publisher={American Geophysical Union (AGU)}\n}", + "data_size_bytes": 5873077683, + "num_files": 2, + "num_images": 3, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.22320373.v2", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "canola_detection_dataset", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_segmentation", + "location": [ + "Miling, WA, Australia", + "York township, WA, Australia" + ], + "lat_lon": [ + "-32.0101, 116.7365" + ], + "country": "Australia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "canola", + "Brassica napus" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "smartphone", + "RGB Canon 600D DSLR camera" + ], + "collection_period": "2018", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16112041", + "citation": "Mckay, M. (2023). *canola_detection_dataset.zip* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.24448516.V1", + "bibtex": "@article{mckay2024focus,\n title={Focus on the Crop Not the Weed: Canola Identification for Precision Weed Management Using Deep Learning},\n author={Mckay, Michael and Danilevicz, Monica F. and Ashworth, Michael B. and Rocha, Roberto Lujan and Upadhyaya, Shriprabha R. and Bennamoun, Mohammed and Edwards, David},\n journal={Remote Sensing},\n volume={16},\n pages={2041},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 4790304130, + "num_files": 2, + "num_images": 16884, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.24448516.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tree_crown_species_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Maoershan Experimental Forest Farm, Heilongjiang Province, China" + ], + "lat_lon": [ + "45.375, 127.533" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Korean pine (Pinus koraiensis Siebold and Zucc.)", + "birch (Betula platyphylla Suk.)", + "Manchurian ash (Fraxinus mandshurica Rupr.)", + "Manchurian walnut (Juglans mandshurica Maxim.)", + "poplar (Populus L.)", + "elm (Ulmus pumila L.)", + "Acer pictum (Acer pictum Thunb)" + ], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "DJI Matrice 600 Pro (DJI, Shenzhen, China) equipped with Reson’s Pika L hyperspectral sensor (Resonon, Bozeman, MT, USA)", + "Riegl miniVUX-1UAV LiDAR sensor (RIEGL, Horn, Austria)" + ], + "collection_period": "August 2021 and 2022", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16234544", + "citation": "Jiayao, W. (2024). *Crown segmentation based on semantic segmentation and CHM* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.27907713.V1", + "bibtex": "@article{wang20243d,\n title={3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification},\n author={Wang, Jiayao and Zhen, Zhen and Zhao, Yuting and Ma, Ye and Zhao, Yinghui},\n journal={Remote Sensing},\n volume={16},\n pages={4544},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 161448889, + "num_files": 20, + "num_images": 4, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.27907713.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "GLC_FCS30D_forest_loss_dynamic", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Global" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Landsat imagery", + "GLAS (Geoscience Laser Altimeter System)", + "GEDI-L4B (Global Ecosystem Dynamics Investigation Level 4A footprint AGB predictions)", + "ASTER GDEM V3" + ], + "collection_period": "1985–2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-16-1353-2024", + "citation": "Liangyun Liu, Xiao Zhang, & Zhehua Li. (2025). *GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022* (Version v2) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15063683", + "bibtex": "@article{zhang2024glc,\n title={GLC_FCS30D: the first global 30 m land-cover dynamics monitoring product with a fine classification system for the period from 1985 to 2022 generated using dense-time-series Landsat imagery and the continuous change-detection method},\n author={Zhang, Xiao and Zhao, Tingting and Xu, Hong and Liu, Wendi and Wang, Jinqing and Chen, Xidong and Liu, Liangyun},\n journal={Earth System Science Data},\n volume={16},\n pages={1353-1381},\n year={2024},\n publisher={Copernicus GmbH}\n}", + "data_size_bytes": 135815129160, + "num_files": 38, + "num_images": 364, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15063683", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "urban_tree_cooling_potential_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Forchheim, Germany" + ], + "lat_lon": [], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "thermal", + "imaging_equipment": [ + "Optris ® PI-450 longwave infrared camera (Optris, Berlin, Germany), resolution 382 × 288 pixels, focal length of 8 mm, temperature range from −20 °C to 100 °C (accuracy +/− 2% or 2 °C), thermal sensitivity of 0.4 °C", + "MicaSense RedEdge-M TM with a focal length of 6 mm (AgEagle Aerial Systems Inc., Wichita, KS, USA)" + ], + "collection_period": "2022", + "platform": "aircraft", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16122059", + "citation": "Samimi, C., & Zandler, H. (2024). *Thermal Flight Data for Forchheim, July 20, 2022, OPTRIS PI-450* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.11046143", + "bibtex": "@article{zandler2024cooling,\n title={Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment},\n author={Zandler, Harald and Samimi, Cyrus},\n journal={Remote Sensing},\n volume={16},\n pages={2059},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 831170774, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.11046143", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "jamari_amazon_gap_dynamics_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Jamari National Forest, Rondônia, Brazil" + ], + "lat_lon": [ + "-9.1667, -63.0167" + ], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "airborne LiDAR" + ], + "collection_period": "2011–2017", + "platform": "airborne", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16132319", + "citation": ", Philip. (2021). *Data from paper: \"Assessment of tropical forests canopy gap dynamics and forest regeneration using airborne LiDAR time series in the Brazilian Amazon\"*. Zenodo. https://doi.org/10.5281/ZENODO.5337079", + "bibtex": "@article{winstanley2024post,\n title={Post-Logging Canopy Gap Dynamics and Forest Regeneration Assessed Using Airborne LiDAR Time Series in the Brazilian Amazon with Attribution to Gap Types and Origins},\n author={Winstanley, Philip and Dalagnol, Ricardo and Mendiratta, Sneha and Braga, Daniel and Galvão, Lênio Soares and Bispo, Polyanna da Conceição},\n journal={Remote Sensing},\n volume={16},\n pages={2319},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 44460968, + "num_files": 8, + "num_images": 19, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.5337079", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "forest_degradation_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Brazilian Amazon, Brazil" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "Airborne LiDAR (Trimble HARRIER 68i system)", + "AISAFenix sensor (Specim, Spectral Imaging, Ltd., Oulu, Finland)" + ], + "collection_period": "2016–2018", + "platform": "airborne", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41597-023-02575-4", + "citation": "Ometto, J. P., Gorgens, B. G., Assis, M., Cantinho, R. Z., Pereira, F. R. de S., & Sato, L. Y. (2021). *L3A - Summary of Airborne LiDAR transects collected by EBA in the Brazilian Amazon* (Version 20210616) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.4968706", + "bibtex": "@article{ometto2023biomass,\n title={A biomass map of the Brazilian Amazon from multisource remote sensing},\n author={Ometto, Jean Pierre and Gorgens, Eric Bastos and de Souza Pereira, Francisca Rocha and Sato, Luciane and de Assis, Mauro Lúcio Rodrigures and Cantinho, Roberta and Longo, Marcos and Jacon, Aline Daniele and Keller, Michael},\n journal={Scientific Data},\n volume={10},\n pages={668},\n year={2023},\n publisher={Springer Science and Business Media LLC}\n}", + "data_size_bytes": 21319159, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.4968706", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "forest_degradation", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Acre, Brazil", + "Rondônia, Brazil" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "Airborne LiDAR (Trimble HARRIER 68i system)", + "AISAFenix sensor (Specim, Spectral Imaging, Ltd., Oulu, Finland)" + ], + "collection_period": "2016–2018", + "platform": "airborne", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41597-023-02575-4", + "citation": "Ometto, J., Gorgens, E. B., Pereira, F. R. de S., Sato, L., Assis, M. L. R., Cantinho, R., Longo, M., Jacon, A. D., & Keller, M. (2023). *L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Acre e Rondônia)* (Version v20230301) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7689909", + "bibtex": "@article{ometto2023biomass,\n title={A biomass map of the Brazilian Amazon from multisource remote sensing},\n author={Ometto, Jean Pierre and Gorgens, Eric Bastos and de Souza Pereira, Francisca Rocha and Sato, Luciane and de Assis, Mauro Lúcio Rodrigures and Cantinho, Roberta and Longo, Marcos and Jacon, Aline Daniele and Keller, Michael},\n journal={Scientific Data},\n volume={10},\n pages={668},\n year={2023},\n publisher={Springer Science and Business Media LLC}\n}", + "data_size_bytes": 20733913577, + "num_files": 2, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7689909", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "urban_tree_functional_traits", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Rennes, France" + ], + "lat_lon": [ + "48.1, -1.68" + ], + "country": "France", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Quercus rubra", + "Platanus acerifolia", + "Acer platanoides", + "Fraxinus excelsior" + ], + "sensor_modality": "", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2021", + "platform": "ground", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16234402", + "citation": "Le Saint, T., Nabucet, J., Sulmon, C., Pellen, J., Adeline, K., & Hubert-Moy, L. (2024). *A spatio-temporal dataset for ecophysiological monitoring of urban trees* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.12751353", + "bibtex": "@article{lesaint2024sensitivity,\n title={Sensitivity Analysis of Sentinel-2 Imagery to Assess Urban Tree Functional Traits: A Physical Approach Based on Local Climate Zones},\n author={Le Saint, Théo and Nabucet, Jean and Lefebvre, Sidonie and Gastellu-Etchegorry, Jean-Philippe and Hubert-Moy, Laurence and Adeline, Karine},\n journal={Remote Sensing},\n volume={16},\n pages={4402},\n year={2024},\n publisher={MDPI}\n}", + "data_size_bytes": 600805, + "num_files": 3, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.12751353", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "ZLRBTerraceMap_1m_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_segmentation", + "location": [ + "Zuli River Basin, Loess Plateau, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "GF-2 RS image (54 multispectral images with 4 m spatial resolution and panchromatic images with 1 m spatial resolution)" + ], + "collection_period": "2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17010012", + "citation": "Guobin Kan, Jie Gong, Bao Wang, Xia Li, Jing Shi, Yutao Ma, Wei Wei, & Jun Zhang. (2024). *A Refined Terrace Extraction Method Based on A Local Opti-mization Model Using GF-2 Images* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14533670", + "bibtex": "@article{kan2025refined,\n title={A Refined Terrace Extraction Method Based on a Local Optimization Model Using GF-2 Images},\n author={Kan, Guobin and Gong, Jie and Wang, Bao and Li, Xia and Shi, Jing and Ma, Yutao and Wei, Wei and Zhang, Jun},\n journal={Remote Sensing},\n volume={17},\n pages={12},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 323612839, + "num_files": 1, + "num_images": 1, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.14533670", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "winter_wheat_aphid_canopy", + "machine_learning_task": "ground_truth", + "agricultural_task": "damage_classification", + "location": [ + "Bc Institute for Breeding and Production of Field Crops, d.d., Zagreb, Croatia" + ], + "lat_lon": [ + "45.7472, 15.9386" + ], + "country": "Croatia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "winter wheat" + ], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "Spectral Evolution ® SR-2500 portable spectroradiometer (Haverhill, MA, USA)" + ], + "collection_period": "mid-April to mid-June 2022", + "platform": "ground", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17050929", + "citation": "Skendžić, S., Novak, H., Lemic, D., Pajač Živković, I., Zovko, M., Lesic, V., & Maričević, M. (2025). *Hyperspectral Canopy Reflectance Data for Aphid-Infested Winter Wheat Classification* (Version v1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14968259", + "bibtex": "@article{skendi2025hyperspectral,\n title={Hyperspectral Canopy Reflectance and Machine Learning for Threshold-Based Classification of Aphid-Infested Winter Wheat},\n author={Skendžić, Sandra and Novak, Hrvoje and Zovko, Monika and Pajač Živković, Ivana and Lešić, Vinko and Maričević, Marko and Lemić, Darija},\n journal={Remote Sensing},\n volume={17},\n pages={929},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 7276730, + "num_files": 6, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.14968259", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "quebec_trees_crown_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Université de Montréal research station, Saint-Hippolyte, Quebec, Canada" + ], + "lat_lon": [ + "45.9882, -74.0058" + ], + "country": "Canada", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Betula papyrifera", + "Acer rubrum", + "Populus grandidentata" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Phantom 4 RTK UAV (DJI Science and Technology Co., Ltd., Shenzhen, China)" + ], + "collection_period": "2021", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.rse.2024.114283", + "citation": "Cloutier, M., Mickaël Germain, & Etienne Laliberté. (2023). *Quebec Trees Dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8148479", + "bibtex": "@article{cloutier2024influence,\n title={Influence of temperate forest autumn leaf phenology on segmentation of tree species from UAV imagery using deep learning},\n author={Cloutier, Myriam and Germain, Mickaël and Laliberté, Etienne},\n journal={Remote Sensing of Environment},\n volume={311},\n pages={114283},\n year={2024},\n publisher={Elsevier BV}\n}", + "data_size_bytes": 149266042447, + "num_files": 9, + "num_images": 12, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.8148479", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "gedi_canopy_height_nc", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "southeastern North Carolina, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2", + "Sentinel-1", + "Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR)", + "National Agriculture Imagery Program (NAIP) aerial photography" + ], + "collection_period": "2018", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17091536", + "citation": "Wang, C., Song, C., Schroeder, T. A., Woodcock, C. E., Pavelsky, T. M., Han, Q., & Yao, F. (2024). *Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina* (Version v0.02) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.11176727", + "bibtex": "@article{wang2025interpretable,\n title={Interpretable Multi-Sensor Fusion of Optical and SAR Data for GEDI-Based Canopy Height Mapping in Southeastern North Carolina},\n author={Wang, Chao and Song, Conghe and Schroeder, Todd A. and Woodcock, Curtis E. and Pavelsky, Tamlin M. and Han, Qianqian and Yao, Fangfang},\n journal={Remote Sensing},\n volume={17},\n pages={1536},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 276225849, + "num_files": 5, + "num_images": 5, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.11176727", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "AGBD_CH_CC_sample_2023_image_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Global", + "N/A", + "N/A" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2", + "Sentinel-1", + "SRTM" + ], + "collection_period": "2016 to 2023", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-nc-4.0", + "documentation": "https://doi.org/10.3390/rs17091594", + "citation": "Weber, M., Beneke, C., Wheeler, C., & Landman, R. (2025). *Global Maps of Aboveground Biomass, Canopy Height and Cover for 2023 at 10 m Resolution from Multi-Sensor Satellite Imagery* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15269923", + "bibtex": "@article{weber2025unified,\n title={Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery},\n author={Weber, Manuel and Beneke, Carly and Wheeler, Clyde},\n journal={Remote Sensing},\n volume={17},\n pages={1594},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 47516254342, + "num_files": 3, + "num_images": 18, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15269923", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "Gilbertiodendron_dewevrei", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Sangha Trinational, Republic of Congo, Congo" + ], + "lat_lon": [], + "country": "Republic of the Congo, Central African Republic, Cameroon", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Gilbertiodendron dewevrei" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2021–2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17091639", + "citation": "Heimpel, E., Harris, D. J., Sanz, C., Morgan, D., Mambounei, J. C., & Ahrends, A. (2025). *Training data from Heimpel et al. (2025) Mapping of Monodominant Gilbertiodendron dewevrei Forest Across the Western Congo Basin Using Sentinel-2 Imagery.* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15342714", + "bibtex": "@article{heimpel2025mapping,\n title={Mapping of Monodominant Gilbertiodendron dewevrei Forest Across the Western Congo Basin Using Sentinel-2 Imagery},\n author={Heimpel, Ellen and Harris, David J. and Mamboueni, Josérald and Morgan, David and Sanz, Crickette and Ahrends, Antje},\n journal={Remote Sensing},\n volume={17},\n pages={1639},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 68577, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15342714", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "maize_seedling_counting_RGB_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Grugliasco, Italy" + ], + "lat_lon": [], + "country": "Italy", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Phantom 4 Pro v2.0 (DJI, Shenzhen, China) drone equipped with its default series RGB camera" + ], + "collection_period": "2025", + "platform": "drone", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17132190", + "citation": "Bumbaca, S. (2025). *The Original Dataset for the Paper \"On the minimum data set requirements for fine-tuning an object detector for arable crop plant counting: A case study on maize seedlings\"* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15235602", + "bibtex": "@article{bumbaca2025minimum,\n title={On the Minimum Dataset Requirements for Fine-Tuning an Object Detector for Arable Crop Plant Counting: A Case Study on Maize Seedlings},\n author={Bumbaca, Samuele and Borgogno-Mondino, Enrico},\n journal={Remote Sensing},\n volume={17},\n pages={2190},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 6501906650, + "num_files": 57, + "num_images": 3, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15235602", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "dynamic_world_forest_built_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Eastern United States", + "Costa Rica" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2022", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17152570", + "citation": "Myers, D. (2023). *Pixel class probabilities investigations data* [Dataset]. Mendeley Data. https://doi.org/10.17632/ZYDS7T4PST.1", + "bibtex": "@article{myers2025increasing,\n title={Increasing the Thematic Resolution for Trees and Built Area in a Global Land Cover Dataset Using Class Probabilities},\n author={Myers, Daniel T. and Oviedo-Vargas, Diana and Daniels, Melinda and Aryal, Yog},\n journal={Remote Sensing},\n volume={17},\n pages={2570},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 623820877, + "num_files": 63, + "num_images": 22, + "source": "doi", + "data_link": "https://doi.org/10.17632/zyds7t4pst.1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "oil_palm_malaysia_mapping", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_classification", + "location": [ + "Malayan Peninsula, Malaysia", + "Borneo, Malaysia" + ], + "lat_lon": [], + "country": "Malaysia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "oil palm" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Landsat 4/5/7/8/9", + "Sentinel-2", + "Sentinel-1 C-band SAR" + ], + "collection_period": "1982 to present", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17162908", + "citation": "Li, C. (2025). *Oil palm mapping dataset* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.28839266.V1", + "bibtex": "@article{li2025integrating,\n title={Integrating Multi-Temporal Landsat and Sentinel Data for Enhanced Oil Palm Plantation Mapping and Age Estimation in Malaysia},\n author={Li, Caihui and Chen, Bangqian and Wang, Xincheng and Ong-Abdullah, Meilina and Wu, Zhixiang and Lan, Guoyu and Azmi Tohiran, Kamil and Amit, Bettycopa and Lai, Hongyan and Wang, Guizhen and Yun, Ting and Kou, Weili},\n journal={Remote Sensing},\n volume={17},\n pages={2908},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 17438662, + "num_files": 20, + "num_images": 10, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.28839266.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "Apocynum_venetum_L_Yuli_county_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Yuli County, Xinjiang, China" + ], + "lat_lon": [ + "41.3, 86.5" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Apocynum venetum L." + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Chinese Gaofen-1 (GF-1) satellite" + ], + "collection_period": "9 August 2023", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-nc-nd-4.0", + "documentation": "https://doi.org/10.3390/rs17173039", + "citation": "Wang, Y. (2025). *A Method for the Extraction of Apocynum venetum L. Spatial Distribution in Yuli County, Xinjiang, via an Improved SegFormer Network* [Dataset]. Mendeley Data. https://doi.org/10.17632/FK8VTDMXFK.1", + "bibtex": "@article{wang2025method,\n title={A Method for the Extraction of Apocynum venetum L. Spatial Distribution in Yuli County, Xinjiang, via an Improved SegFormer Network},\n author={Wang, Yixuan and Wang, Hong and Wang, Xinhui},\n journal={Remote Sensing},\n volume={17},\n pages={3039},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 407430591, + "num_files": 1, + "num_images": 811, + "source": "doi", + "data_link": "https://doi.org/10.17632/fk8vtdmxfk.1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "BelizeCCDCSMA_mangrove_change_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Belize, Belize" + ], + "lat_lon": [], + "country": "Belize", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Landsat archive" + ], + "collection_period": "2017 to 2024", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17203396", + "citation": "Evans, C., Carey, L., Guerra, F., Cherrington, E., & Correa, E. (2025). *Data for: Cloud-based solutions for monitoring coastal ecosystems and prioritization of restoration efforts across Belize* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15328468", + "bibtex": "@article{evans2025cloud,\n title={Cloud-Based Solutions for Monitoring Coastal Ecosystems and the Prioritization of Restoration Efforts Across Belize},\n author={Evans, Christine and Carey, Lauren and Guerra, Florencia and Cherrington, Emil A. and Correa, Edgar and Quintero, Diego},\n journal={Remote Sensing},\n volume={17},\n pages={3396},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 96190215, + "num_files": 3, + "num_images": 39, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15328468", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "hainan_canopy_height_rf", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Hainan Tropical Rainforest National Park, Hainan Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "VLP-16 LiDAR (Velodyne LiDAR Inc., San Jose, CA, USA)", + "DJI Mavic 3M drone (DJI Innovations, Shenzhen, China)" + ], + "collection_period": "2003 to 2023", + "platform": "mixed", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17060966", + "citation": "Qiu, Z., Ling, Q., Chen, Y., & Zhaode, Y. (2025). *Canopy Height Monitoring Data and Model of Hainan Tropical Rainforest National Park* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.28138559.V1", + "bibtex": "@article{ling2025monitoring,\n title={Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion},\n author={Ling, Qingping and Chen, Yingtan and Feng, Zhongke and Pei, Huiqing and Wang, Cai and Yin, Zhaode and Qiu, Zixuan},\n journal={Remote Sensing},\n volume={17},\n pages={966},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 74468953, + "num_files": 4, + "num_images": 12, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.28138559.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "avp_tree_diameter_measurement_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Forchtenstein, Lower Austria, Austria" + ], + "lat_lon": [ + "47.73691, 16.25093" + ], + "country": "Austria", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Picea abies", + "Abies alba", + "Pinus sylvestris", + "Fagus sylvatica", + "Alnus glutinosa", + "Ulmus glabra" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Apple Vision Pro (256 GB model, Apple Inc., Cupertino, CA, USA)" + ], + "collection_period": "August 2024", + "platform": "mixed", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17010141", + "citation": "Ofner-Graff, T., Sarkleti, V., Svazek, P., Tockner, A., Witzmann, S., Moik, L., Krassnitzer, R., Gollob, C., Ritter, T., Kühmaier, M., Stampfer, K., & Nothdurft, A. (2024). *AVP-LAUT – Tree diameter data collected with Apple Vision Pro from Austrian forest Inventory plots* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14040646", + "bibtex": "@article{ofnergraff2025potential,\n title={Potential of Apple Vision Pro for Accurate Tree Diameter Measurements in Forests},\n author={Ofner-Graff, Tobias and Sarkleti, Valentin and Svazek, Philip and Tockner, Andreas and Witzmann, Sarah and Moik, Lukas and Kraßnitzer, Ralf and Gollob, Christoph and Ritter, Tim and Kühmaier, Martin and Stampfer, Karl and Nothdurft, Arne},\n journal={Remote Sensing},\n volume={17},\n pages={141},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 14450501873, + "num_files": 4, + "num_images": 15, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.14040646", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tree_species_mapping_central_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Centre-Val de Loire region, France" + ], + "lat_lon": [], + "country": "France", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "oak", + "pine", + "beech", + "chestnut" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2 (S2-A and S2-B)" + ], + "collection_period": "2019 and 2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17071190", + "citation": "Mouret, F., Université d'Orléans, Centre d'Études Spatiales de la Biosphère, Morin, D., Planells, M., & VINCENT-BARBAROUX, C. (2025). *Dataset : Tree species classification at the pixel-level using deep learning and multispectral time series in an imbalanced context* (Version 1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15001875", + "bibtex": "@article{mouret2025tree,\n title={Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context},\n author={Mouret, Florian and Morin, David and Planells, Milena and Vincent-Barbaroux, Cécile},\n journal={Remote Sensing},\n volume={17},\n pages={1190},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 624233112, + "num_files": 1, + "num_images": 7, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15001875", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "zenodo_11206113_boreal_trails_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "northeastern Alberta, Canada" + ], + "lat_lon": [], + "country": "Canada", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "Riegl VQ-1560ii system", + "Zenmuse L1 LiDAR sensor aboard a DJI Matrice 300 RTK", + "DJI Zenmuse P1 sensor (full-frame 45-megapixel camera with a 35 mm lens)" + ], + "collection_period": "summer of 2022", + "platform": "drone", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17091539", + "citation": "Terentieva, I. (2024). *Supplementary: Mapping Trails and Tracks in the Boreal Forest using LiDAR and Convolutional Neural Networks* (Version 1.0.0). Zenodo. https://doi.org/10.5281/ZENODO.11206113", + "bibtex": "@article{mcdermid2025mapping,\n title={Mapping Trails and Tracks in the Boreal Forest Using LiDAR and Convolutional Neural Networks},\n author={McDermid, Gregory J. and Terenteva, Irina and Chan, Xue Yan},\n journal={Remote Sensing},\n volume={17},\n pages={1539},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 393871423, + "num_files": 22, + "num_images": 8, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.11206113", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "mowing_events_austria_2023_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Austria, Austria" + ], + "lat_lon": [], + "country": "Austria", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grassland" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2" + ], + "collection_period": "2023", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17101769", + "citation": "Miletich, P., Hirschmugl, M., & Schippel, A. (2025). *Mowing Events in Austria in 2023* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15350942", + "bibtex": "@article{miletich2025open,\n title={Open and Free Sentinel-2 Mowing Event Data for Austria},\n author={Miletich, Petra and Kirchmair, Marco and Deutscher, Janik Gregory and Schippl, Alexander and Hirschmugl, Manuela},\n journal={Remote Sensing},\n volume={17},\n pages={1769},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 282476334, + "num_files": 2, + "num_images": 2, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15350942", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "timberline_delineation_west_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "West Siberian Plain, Russia", + "Alaska region, USA" + ], + "lat_lon": [], + "country": "Russia, USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Landsat (archived in Google Satellite)", + "Maxar satellite images" + ], + "collection_period": "2019", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17122002", + "citation": "Zhang, T. (2023). *timberline mapped for West Siberia Plain and Alaska* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10023630", + "bibtex": "@article{zhang2025region,\n title={A Region-Growing Segmentation Approach to Delineating Timberline from Satellite-Derived Tree Fractional Cover Products},\n author={Zhang, Tianqi and Kumar, Jitendra and Hoffman, Forrest M. and Ivanov, Valeriy and Wang, Jingfeng and Sheshukov, Aleksey Y. and Zhou, Wenbo and Montesano, Paul and Liu, Desheng},\n journal={Remote Sensing},\n volume={17},\n pages={2002},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 41756955, + "num_files": 3, + "num_images": 3, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10023630", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sentinel2_soc_prediction_salt_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Lezíria de Vila Franca, Portugal" + ], + "lat_lon": [], + "country": "Portugal", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rice", + "tomato" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2 Multi-Spectral Instrument (MSI) at Level 2A" + ], + "collection_period": "1 June 2022 to 1 June 2023", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17162877", + "citation": "Paz, A. M., Farzamian, M., & Antunes, J. (2023). *Soil grid data for 4 agricultural fields in PT (ECe; soil organic carbon, pH)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10118120", + "bibtex": "@article{farzamian2025predicting,\n title={Predicting Soil Organic Carbon from Sentinel-2 Imagery and Regional Calibration Approach in Salt-Affected Agricultural Lands: Feasibility and Influence of Soil Properties},\n author={Farzamian, Mohammad and Castanheira, Nádia and Gonçalves, Maria C. and Freitas, Pedro and Saberioon, Mohammadmehdi and Ramos, Tiago B. and Antunes, João and Paz, Ana Marta},\n journal={Remote Sensing},\n volume={17},\n pages={2877},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 31522909, + "num_files": 6, + "num_images": 5, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10118120", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "caatinga_phenology_time_series", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Caatinga, Northeast Brazil", + "Cerrado, South-eastern Brazil" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "digital hemispherical Mobotix Q 24 lens cameras (Mobotix AG-Germany, Winnweiler, Germany)" + ], + "collection_period": "four-year study period", + "platform": "phenocam", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17162883", + "citation": "Koolen, S. P., Godlee, J., Alberton, B., Ramos, D., Moura, M. S. B. de, Cerdeira Morellato, L. P., & Dexter, K. (2025). *Dataset: Coexistence of trees, shrubs and grasses creates a complex pic-ture of land surface phenology in tropical dry ecosystems* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15616374", + "bibtex": "@article{koolen2025coexistence,\n title={The Coexistence of Trees, Shrubs, and Grasses Creates a Complex Picture of Land Surface Phenology in Dry Tropical Ecosystems},\n author={Koolen, Stephanie P. and Godlee, John L. and Alberton, Bruna and Ramos, Desirée Marques and Moura, Magna Soelma Beserra and Morellato, Leonor Patricia C. and Dexter, Kyle G.},\n journal={Remote Sensing},\n volume={17},\n pages={2883},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 971586, + "num_files": 6, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15616374", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "dfc30_bare_earth_dem_super_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "France, France" + ], + "lat_lon": [], + "country": "France", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Copernicus GLO-30 DEM data", + "aerial guidance imagery" + ], + "collection_period": "", + "platform": "aerial", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17213591", + "citation": "Cai, X. (2026). *DFC30* (Version v3) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18426236", + "bibtex": "@article{cai2025jspsr,\n title={JSPSR: Joint Spatial Propagation Super-Resolution Networks for Enhancement of Bare-Earth Digital Elevation Models from Global Data},\n author={Cai, Xiandong and Wilson, Matthew D.},\n journal={Remote Sensing},\n volume={17},\n pages={3591},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 44199724960, + "num_files": 11, + "num_images": 55734, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18426236", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "Loess_plateau_terrace_time_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "land_use_segmentation", + "location": [ + "Loess Plateau, China" + ], + "lat_lon": [ + "33.7167, 100.9000" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2A and 2B satellites (Multispectral Imager, 13 spectral bands, 10 m resolution)" + ], + "collection_period": "2017 to 2025", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18010085", + "citation": "Guobin, K., Jianhua, X., Benli, L., Bao, W., Chenchen, H., & Hong, Y. (2025). *Remote Sensing Extraction and Spatiotemporal Change Analysis of Time- Series Terraces in Complex Terrain on the Loess Plateau Based on a New Swin Transformer Dual-Branch Deformable Boundary Network (STDBNet)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18052358", + "bibtex": "@article{kan2026remote,\n title={Remote Sensing Extraction and Spatiotemporal Change Analysis of Time-Series Terraces in Complex Terrain on the Loess Plateau Based on a New Swin Transformer Dual-Branch Deformable Boundary Network (STDBNet)},\n author={Kan, Guobin and Xiao, Jianhua and Liu, Benli and Wang, Bao and He, Chenchen and Yang, Hong},\n journal={Remote Sensing},\n volume={18},\n pages={85},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 507172, + "num_files": 1, + "num_images": 9, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.18052358", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "FA_CTNet_tree_species_classification", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Schorfheide-Chorin, Germany", + "Hainich-Dün, Germany", + "Schwäbische Alb, Germany" + ], + "lat_lon": [], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "lidar", + "imaging_equipment": [ + "TLS", + "unmanned aerial laser scanning (ULS)", + "mobile laser scanning (MLS)" + ], + "collection_period": "", + "platform": "mixed", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1038/s41597-025-06421-7", + "citation": "Griese, N., Ritzert, M., & Nölke, N. (2025). *BioDiv-3DTrees: A Large Labelled Single Tree Point Cloud, QSM and Tree Graph Dataset* (Version 1.0) [Dataset]. GRO.data. https://doi.org/10.25625/8PB1IF", + "bibtex": "@article{griese2025large,\n title={A large dataset of labelled single tree point clouds, QSMs and tree graphs},\n author={Griese, Nils and Ritzert, Martin and Nölke, Nils},\n journal={Scientific Data},\n volume={12},\n pages={1953},\n year={2025},\n publisher={Springer Science and Business Media LLC}\n}", + "data_size_bytes": 130395095357, + "num_files": 6, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.25625/8PB1IF", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "polsar_landcover_sfbay_flevo_classification", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_classification", + "location": [ + "Flevoland region, Netherlands", + "San Francisco Bay Area, California, USA" + ], + "lat_lon": [], + "country": "Netherlands, USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "alfalfa", + "canola", + "sugar beet", + "potato", + "pea", + "stem bean", + "wheat A/B/C", + "barley" + ], + "sensor_modality": "sar", + "imaging_equipment": [ + "AIRSAR L-band system", + "RADARSAT-2 C-band system" + ], + "collection_period": "August 1989", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "mit", + "documentation": "https://doi.org/10.1080/15481603.2020.1853948", + "citation": "", + "bibtex": "@article{ahishali2020classification,\n title={Classification of polarimetric SAR images using compact convolutional neural networks},\n author={Ahishali, Mete and Kiranyaz, Serkan and Ince, Turker and Gabbouj, Moncef},\n journal={GIScience & Remote Sensing},\n volume={58},\n pages={28-47},\n year={2020},\n publisher={Informa UK Limited}\n}", + "data_size_bytes": 242027779, + "num_files": 49, + "num_images": 0, + "source": "doi", + "data_link": "https://github.com/meteahishali/sar-classification", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "potato_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_detection", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "potato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3905, + "classes": [ + "Black Scurf", + "Blackleg", + "Blackspot Bruising", + "Brown Rot", + "Common Scab", + "Dry Rot", + "Healthy Potatoes", + "Miscellaneous", + "Pink Rot", + "Soft Rot" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16131408", + "citation": "Islam, S., & Afrin, T. (2025). *PotatoCare: Deep learning based potato disease dataset* [Dataset]. Mendeley Data. https://doi.org/10.17632/7VM7XSKFG4.2", + "bibtex": "@article{gkountakos2026potato,\n title={Potato-Free: Eliminating Model Training for Disease Detection and Localization in Potato Tubers},\n author={Gkountakos, Konstantinos and Pasios, Stefanos and Ioannidis, Konstantinos and Demestichas, Konstantinos and Vrochidis, Stefanos and Kompatsiaris, Ioannis},\n journal={Agriculture},\n volume={16},\n pages={1408},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 93105088, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/potato_disease_classification", + "examples_image_url": "/img/agml/sample_images/potato_disease_classification_sample.webp" + }, + { + "name": "Plant_leaf_diseases_dataset_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_detection", + "location": [ + "Tainan, Taiwan, Taiwan" + ], + "lat_lon": [], + "country": "Taiwan", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "sweet potato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Fujifilm GFX100s", + "Fujifilm X100F" + ], + "collection_period": "", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 55448, + "augmented_num_images": 61486, + "augmented_zip_size_bytes": 944549209, + "classes": [ + "Apple___Apple_scab", + "Apple___Black_rot", + "Apple___Cedar_apple_rust", + "Apple___healthy", + "Background_without_leaves", + "Blueberry___healthy", + "Cherry___Powdery_mildew", + "Cherry___healthy", + "Corn___Cercospora_leaf_spot Gray_leaf_spot", + "Corn___Common_rust", + "Corn___Northern_Leaf_Blight", + "Corn___healthy", + "Grape___Black_rot", + "Grape___Esca_(Black_Measles)", + "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", + "Grape___healthy", + "Orange___Haunglongbing_(Citrus_greening)", + "Peach___Bacterial_spot", + "Peach___healthy", + "Pepper,_bell___Bacterial_spot", + "Pepper,_bell___healthy", + "Potato___Early_blight", + "Potato___Late_blight", + "Potato___healthy", + "Raspberry___healthy", + "Soybean___healthy", + "Squash___Powdery_mildew", + "Strawberry___Leaf_scorch", + "Strawberry___healthy", + "Tomato___Bacterial_spot", + "Tomato___Early_blight", + "Tomato___Late_blight", + "Tomato___Leaf_Mold", + "Tomato___Septoria_leaf_spot", + "Tomato___Spider_mites Two-spotted_spider_mite", + "Tomato___Target_Spot", + "Tomato___Tomato_Yellow_Leaf_Curl_Virus", + "Tomato___Tomato_mosaic_virus", + "Tomato___healthy" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16080854", + "citation": "J, A. R. U. N. P. A. N. D. I. A. N. (2019). *Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network* [Dataset]. Mendeley. https://doi.org/10.17632/TYWBTSJRJV.1", + "bibtex": "@article{yang2026label,\n title={Label-Free Deep Learning with Feature Adaptation for Crop Anomaly Detection on Small Datasets},\n author={Yang, Ming-Der and Lee, Tzu-Han and Tseng, Hsin-Hung and Su, Tung-Ching and Hsu, Yu-Chun},\n journal={Agriculture},\n volume={16},\n pages={854},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 863481329, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Plant_leaf_diseases_dataset_classification", + "examples_image_url": "/img/agml/sample_images/Plant_leaf_diseases_dataset_classification_sample.webp" + }, + { + "name": "ws_dino_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "University of Bonn, Germany", + "southern Germany, Germany" + ], + "lat_lon": [], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "sugar beet", + "sorghum" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI M600 UAV, PhaseOne iXM-100 RGB camera", + "DJI Mavic 2 Pro UAV" + ], + "collection_period": "", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 19, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16101105", + "citation": "", + "bibtex": "@article{zhou2026ws,\n title={WS-DINO: A DINOv2-Based Weed Segmentation Method with Feature Priors and Spatial Fusion},\n author={Zhou, Hongsheng and Liu, Jiangping and Wu, Rigeng and Zhao, Baoping},\n journal={Agriculture},\n volume={16},\n pages={1105},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 192721915, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/ws_dino_weed_segmentation", + "examples_image_url": "/img/agml/sample_images/ws_dino_weed_segmentation_sample.webp" + }, + { + "name": "mango_fruit_stem_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Zhongyang Village, Hainan Province, China", + "Fuhua Farm, Hainan Province, China" + ], + "lat_lon": [ + "18.5667, 109.9167", + "18.6167, 109.6167" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "mango" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Xiaomi 14 smartphone (handheld)", + "Redmi K40 Pro smartphone (handheld)" + ], + "collection_period": "7-8 December 2024", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 1782, + "classes": [ + "fruit", + "stem" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16171888", + "citation": "", + "bibtex": "@article{lu2026feature,\n title={A Feature Enhancement Framework for Joint Mango Fruit and Stem Detection in Complex Orchard Environments},\n author={Lu, Jiahuan and Deng, Qihan and Zheng, Weiping and Cai, Binglong and Zeng, Shan and Li, Jiehao},\n journal={Agriculture},\n volume={16},\n pages={1888},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 7232660940, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/mango_fruit_stem_detection", + "examples_image_url": "/img/agml/sample_images/mango_fruit_stem_detection_sample.webp" + }, + { + "name": "OccPepSeg_YOLO_occluded_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "Otog Front Banner, Inner Mongolia Autonomous Region, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "pepper" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "ORBBEC Gemini 335L (1280x800 pixels)" + ], + "collection_period": "June to July 2025", + "platform": "", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 1500, + "augmented_num_images": 6000, + "augmented_zip_size_bytes": 930596215, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16171646", + "citation": "", + "bibtex": "@article{yu2026occpepseg,\n title={OccPepSeg-YOLO for Instance Segmentation of Occluded Peppers in Field Images},\n author={Yu, Xinran and Jiang, Mingxi and Gao, Fei and Fan, Yize and Bai, Yanyan and Peng, Zhigang and Lu, Qi and Liu, Qian and Xu, Shengyong},\n journal={Agronomy},\n volume={16},\n pages={1646},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 831010277, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/OccPepSeg_YOLO_occluded_segmentation", + "examples_image_url": "/img/agml/sample_images/OccPepSeg_YOLO_occluded_segmentation_sample.webp" + }, + { + "name": "CosegPP_Buckwheat_Sunflower_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "University of Nebraska-Lincoln, Nebraska, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Buckwheat", + "Sunflower" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "LemnaTec Scanalyzer 3D plant phenotyping system" + ], + "collection_period": "3 July to 18 August 2019", + "platform": "ground", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 496, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0257001", + "citation": "", + "bibtex": "@article{quiones2021multi,\n title={Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping},\n author={Quiñones, Rubi and Munoz-Arriola, Francisco and Choudhury, Sruti Das and Samal, Ashok},\n journal={PLOS ONE},\n volume={16},\n pages={e0257001},\n year={2021},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 408198410, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/CosegPP_Buckwheat_Sunflower_segmentation", + "examples_image_url": "/img/agml/sample_images/CosegPP_Buckwheat_Sunflower_segmentation_sample.webp" + }, + { + "name": "grapevine_bunch_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Vairão, Portugal" + ], + "lat_lon": [ + "41.4034, -2.1740" + ], + "country": "Portugal", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Xiaomi Redmi Note 7 smartphone (dual camera, 8000 × 6000 pixels, handheld)" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 10010, + "classes": [ + "Bunch" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy13041120", + "citation": "", + "bibtex": "@article{pinheiro2023deep,\n title={Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions},\n author={Pinheiro, Isabel and Moreira, Germano and Queirós da Silva, Daniel and Magalhães, Sandro and Valente, António and Moura Oliveira, Paulo and Cunha, Mário and Santos, Filipe},\n journal={Agronomy},\n volume={13},\n pages={1120},\n year={2023},\n publisher={MDPI}\n}", + "zip_size_bytes": 673158584, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/grapevine_bunch_detection", + "examples_image_url": "/img/agml/sample_images/grapevine_bunch_detection_sample.webp" + }, + { + "name": "grapevine_bunch_condition_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Vairão, Portugal" + ], + "lat_lon": [ + "41.4034, -2.1740" + ], + "country": "Portugal", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Xiaomi Redmi Note 7 smartphone (dual camera, 8000 × 6000 pixels, handheld)" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 10010, + "classes": [ + "OptimalBunch", + "DamagedBunch" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy13041120", + "citation": "", + "bibtex": "@article{pinheiro2023deep,\n title={Deep Learning YOLO-Based Solution for Grape Bunch Detection and Assessment of Biophysical Lesions},\n author={Pinheiro, Isabel and Moreira, Germano and Queirós da Silva, Daniel and Magalhães, Sandro and Valente, António and Moura Oliveira, Paulo and Cunha, Mário and Santos, Filipe},\n journal={Agronomy},\n volume={13},\n pages={1120},\n year={2023},\n publisher={MDPI}\n}", + "zip_size_bytes": 673158691, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/grapevine_bunch_condition_detection", + "examples_image_url": "/img/agml/sample_images/grapevine_bunch_condition_detection_sample.webp" + }, + { + "name": "paddy_weed_target_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rice" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon IXUS 1000 HS (EF-S 36–360 mm f/3.4–5.6 IS STM) handheld" + ], + "collection_period": "2018-2019", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 358, + "classes": [ + "weed" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0294709", + "citation": "", + "bibtex": "@article{deng2023weed,\n title={Weed target detection at seedling stage in paddy fields based on YOLOX},\n author={Deng, Xiangwu and Qi, Long and Liu, Zhuwen and Liang, Song and Gong, Kunsong and Qiu, Guangjun},\n journal={PLOS ONE},\n volume={18},\n pages={e0294709},\n year={2023},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 12806459, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/paddy_weed_target_detection", + "examples_image_url": "/img/agml/sample_images/paddy_weed_target_detection_sample.webp" + }, + { + "name": "AppleTreeDataset_intertwined_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_segmentation", + "location": [ + "National Institute of Horticultural and Herbal Science, Jeonju, South Korea" + ], + "lat_lon": [], + "country": "South Korea", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "apple" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "iPhone 6, 2448 × 3264 pixels, handheld" + ], + "collection_period": "August to September 2021", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 150, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture13112097", + "citation": "", + "bibtex": "@article{la2023deep,\n title={Deep Learning-Based Segmentation of Intertwined Fruit Trees for Agricultural Tasks},\n author={La, Young-Jae and Seo, Dasom and Kang, Junhyeok and Kim, Minwoo and Yoo, Tae-Woong and Oh, Il-Seok},\n journal={Agriculture},\n volume={13},\n pages={2097},\n year={2023},\n publisher={MDPI}\n}", + "zip_size_bytes": 3042182288, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/AppleTreeDataset_intertwined_segmentation", + "examples_image_url": "/img/agml/sample_images/AppleTreeDataset_intertwined_segmentation_sample.webp" + }, + { + "name": "tree_diameter_phone_images", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Laurel Creek Conservation Area, Waterloo, Ontario, Canada", + "Beechwoods Nature Reserve, Cambridge, United Kingdom", + "Van Cortlandt Park Preserve, New York, NY, United States" + ], + "lat_lon": [], + "country": "Canada; United Kingdom; United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "Huawei P30 Pro smartphone RGB camera with time-of-flight depth (LiDAR) sensor" + ], + "collection_period": "three seasons, 2021-2022", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs15030772", + "citation": "Holcomb, A., Tong, L., & Keshav, S. (2022). *Data from: Robust single-image tree diameter estimation with mobile phones* (Version 2) [Dataset]. Dryad. https://doi.org/10.5061/DRYAD.VDNCJSXXJ", + "bibtex": "@article{holcomb2023robust,\n title={Robust Single-Image Tree Diameter Estimation with Mobile Phones},\n author={Holcomb, Amelia and Tong, Linzhe and Keshav, Srinivasan},\n journal={Remote Sensing},\n volume={15},\n pages={772},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 130924487, + "num_files": 259, + "num_images": 126, + "source": "doi", + "data_link": "https://doi.org/10.5061/dryad.vdncjsxxj", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "soybean_leaf_disease_dryad", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "E.V. Smith Agricultural Research Station, Tallassee, Alabama, USA", + "Cullars Rotation, Auburn, Alabama, USA", + "Brewton Agricultural Research Unit, Brewton, Alabama, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "soybean" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon EOS 7D Mark II DSLR", + "Motorola Moto Z2 Play smartphone" + ], + "collection_period": "2020-2021 soybean seasons", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/agronomy16020218", + "citation": "Bevers, N., Sikora, E. J., & Hardy, N. B. (2022). *Pictures of diseased soybean leaves by category captured in field and with controlled backgrounds: Auburn soybean disease image dataset (ASDID)* (Version 6) [Dataset]. Dryad. https://doi.org/10.5061/DRYAD.41NS1RNJ3", + "bibtex": "@article{li2026soybean,\n title={Soybean Leaf Disease Recognition Methods Based on Hyperparameter Transfer and Progressive Fine-Tuning of Large Models},\n author={Li, Xiaoming and Bian, Wenxue and Yang, Boyu and Li, Yongguang and Wang, Shiqi and Qin, Ning and Ye, Shanglong and Bao, Zunyang and Sun, Hongmin},\n journal={Agronomy},\n volume={16},\n pages={218},\n year={2026},\n publisher={MDPI}\n}", + "data_size_bytes": 43357469481, + "num_files": 12, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5061/dryad.41ns1rnj3", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "desert_vegetation_uav_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Suhaitu Gacha, Alxa League, Inner Mongolia Autonomous Region, China" + ], + "lat_lon": [ + "40.166944, 104.805833" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "tamarisk", + "Populus euphratica", + "Nitraria", + "Haloxylon ammodendron (saxaul)" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "UAV RGB camera" + ], + "collection_period": "", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17040691", + "citation": "Hua, S. (2025). *High-resolution UAV desert vegetation dataset* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.28387949.V1", + "bibtex": "@article{hua2025gdpgo,\n title={GDPGO-SAM: An Unsupervised Fine Segmentation of Desert Vegetation Driven by Grounding DINO Prompt Generation and Optimization Segment Anything Model},\n author={Hua, Shuzhen and Yang, Biao and Zhang, Xinchang and Qi, Ji and Su, Fengxi and Sun, Jing and Ruan, Yongjian},\n journal={Remote Sensing},\n volume={17},\n pages={691},\n year={2025},\n publisher={MDPI}\n}", + "data_size_bytes": 793395256, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.28387949.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "asturian_cider_apple_variety_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "Siero, Asturias, Spain" + ], + "lat_lon": [], + "country": "Spain", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "apple (cider varieties)" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy12112856", + "citation": "García Cortés, S., Diaz, A. M., Oliveira Prendes, J. A., & Bello Garcia, A. (2022). *2022-v2-9classes-AsturApple-SIZE224-train-dev-test.hdf5* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.20318241.V1", + "bibtex": "@article{garcacorts2022transfer,\n title={Transfer Learning with Convolutional Neural Networks for Cider Apple Varieties Classification},\n author={García Cortés, Silverio and Menéndez Díaz, Agustín and Oliveira Prendes, José Alberto and Bello García, Antonio},\n journal={Agronomy},\n volume={12},\n pages={2856},\n year={2022},\n publisher={MDPI}\n}", + "data_size_bytes": 608790396, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.20318241.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "grapemots_uav_vineyard_mots", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Tomiño, Pontevedra, Galicia, Spain" + ], + "lat_lon": [ + "41.955139, -8.794778" + ], + "country": "Spain", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grapevine" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Phantom 4 RTK UAV RGB camera, video, 3 m above ground" + ], + "collection_period": "2023 harvest campaign", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2024.110432", + "citation": "Mar, A.-S., Kaiwen, W., Zhen, C., Sergio, V., & João, V. (2024). *MOTS-annotated UAV Vineyard Dataset captured using Multiple Perspectives to avoid Leaf Occlusion for Object Detection and Tracking* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10625595", + "bibtex": "@article{Ariza_Sent_s_2024, title={GrapeMOTS: UAV vineyard dataset with MOTS grape bunch annotations recorded from multiple perspectives for enhanced object detection and tracking}, volume={54}, ISSN={2352-3409}, url={http://dx.doi.org/10.1016/j.dib.2024.110432}, DOI={10.1016/j.dib.2024.110432}, journal={Data in Brief}, publisher={Elsevier BV}, author={Ariza-Sentís, Mar and Wang, Kaiwen and Cao, Zhen and Vélez, Sergio and Valente, João}, year={2024}, month=June, pages={110432} }", + "data_size_bytes": 87362403499, + "num_files": 22, + "num_images": 4400, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10625595", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "uav_tree_phenology_wageningen", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Forest northeast of Wageningen, Gelderland, Netherlands" + ], + "lat_lon": [ + "51.993725, 5.718241" + ], + "country": "Netherlands", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "MicaSense multispectral camera on UAV" + ], + "collection_period": "2021", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15143599", + "citation": "Kleinsmann, J., Kooistra, L., & Verbesselt, J. (2023). *Monitoring Individual Tree Phenology in a Multi-Species Forest using High Resolution UAV Images*. https://doi.org/10.5281/ZENODO.8008028", + "bibtex": "@article{kleinsmann2023monitoring,\n title={Monitoring Individual Tree Phenology in a Multi-Species Forest Using High Resolution UAV Images},\n author={Kleinsmann, Jasper and Verbesselt, Jan and Kooistra, Lammert},\n journal={Remote Sensing},\n volume={15},\n pages={3599},\n year={2023},\n publisher={MDPI}\n}", + "data_size_bytes": 11349204939, + "num_files": 1, + "num_images": 17, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.8008028", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "alpine_meadow_restoration_hyperspectral", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [ + "Ebao Town, Qilian County, Qinghai Province, China" + ], + "lat_lon": [ + "37.949864, 100.941508" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "SOC710VP hyperspectral camera on tripod" + ], + "collection_period": "July 2023", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [ + "Active", + "Healthy", + "Inactive", + "Recovering" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture14071097", + "citation": "Luo, W. (2024). *Hyperspectral images of patches at different stages of degraded alpine meadows* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10990325", + "bibtex": "@article{Luo_2024, title={Identifying the Restoration Stages of Degraded Alpine Meadow Patches Using Hyperspectral Imaging and Machine Learning Techniques}, volume={14}, ISSN={2077-0472}, url={http://dx.doi.org/10.3390/agriculture14071097}, DOI={10.3390/agriculture14071097}, number={7}, journal={Agriculture}, publisher={MDPI AG}, author={Luo, Wei and Wang, Lu and Cui, Lulu and Zheng, Min and Li, Xilai and Li, Chengyi}, year={2024}, month=July, pages={1097} }", + "data_size_bytes": 1554320902, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10990325", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "pvy_potato_uav_hyperspectral", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "disease_detection", + "location": [ + "BART Farm, Montana State University, Bozeman, MT" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "potato" + ], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "Resonon Pika L hyperspectral camera on Vector hexacopter UAV" + ], + "collection_period": "2023", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17101735", + "citation": "Nesar, S., Nugent, P., & Whitaker, B. (2025). *Normalized Data for UAV-based Hyperspectral Imaging for PVY Detection* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.15417759", + "bibtex": "@article{Nesar_2025, title={Unmanned Aerial Vehicle-Based Hyperspectral Imaging for Potato Virus Y Detection: Machine Learning Insights}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17101735}, DOI={10.3390/rs17101735}, number={10}, journal={Remote Sensing}, publisher={MDPI AG}, author={Nesar, Siddat B. and Nugent, Paul W. and Zidack, Nina K. and Whitaker, Bradley M.}, year={2025}, month=May, pages={1735} }", + "data_size_bytes": 27233046936, + "num_files": 1, + "num_images": 38, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.15417759", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "corn_leaf_point_cloud_completion", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15225289", + "citation": "Zhang, Y., & Su, W. (2022). *Corn 3D point clouds* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.21384186.V1", + "bibtex": "@article{Zhang_2023, title={Completing 3D Point Clouds of Thin Corn Leaves for Phenotyping Using 3D Gridding Convolutional Neural Networks}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15225289}, DOI={10.3390/rs15225289}, number={22}, journal={Remote Sensing}, publisher={MDPI AG}, author={Zhang, Ying and Su, Wei and Tao, Wancheng and Li, Ziqian and Huang, Xianda and Zhang, Ziyue and Xiong, Caisen}, year={2023}, month=Nov, pages={5289} }", + "data_size_bytes": 13044936, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.21384186.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "field_cotton_point_cloud", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [ + "Huaxing Farm, Changji, Xinjiang, China" + ], + "lat_lon": [ + "44.033333, 87.500000" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "UAV RGB camera" + ], + "collection_period": "25 October 2025", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16171619", + "citation": "Hao. (2026). *pc_cotton_data* (Version 1) [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.33195915.V1", + "bibtex": "@article{Qiu_2026, title={Low-Cost and Rapid Construction of 3D Point Clouds for Field-Grown Cotton and Evaluation of Canopy-Level Traits}, volume={16}, ISSN={2073-4395}, url={http://dx.doi.org/10.3390/agronomy16171619}, DOI={10.3390/agronomy16171619}, number={17}, journal={Agronomy}, publisher={MDPI AG}, author={Qiu, Hao and Meng, Xiaoyan and Zhao, Yunjie and Wang, Yuxiang and Niu, Haoyuan and Yu, Liang and Yin, Shuai}, year={2026}, month=Aug, pages={1619} }", + "data_size_bytes": 380701537, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.33195915.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "maize_planter_field_point_cloud", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [ + "Srednji Globodol, Slovenia" + ], + "lat_lon": [ + "45.851750, 15.047944" + ], + "country": "Slovenia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "point_cloud", + "imaging_equipment": [], + "collection_period": "2022-2023", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy15020462", + "citation": "Petrović, I., & Vučajnk, F. (2025). *Point clouds of the maize field to evaluate the influence of planting speed on the variability of plant spacing and ear parameters* (Version v.01) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14640577", + "bibtex": "@article{Petrovi__2025, title={The Influence of Planting Speed of a Maize Vacuum Planter on Plant Spacing Variability and Ear Parameters}, volume={15}, ISSN={2073-4395}, url={http://dx.doi.org/10.3390/agronomy15020462}, DOI={10.3390/agronomy15020462}, number={2}, journal={Agronomy}, publisher={MDPI AG}, author={Petrović, Igor and Vučajnk, Filip and Trdan, Stanislav and Bernik, Rajko and Vidrih, Matej}, year={2025}, month=Feb, pages={462} }", + "data_size_bytes": 11723547296, + "num_files": 2, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.14640577", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "scallion_posture_images", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_detection", + "location": [ + "Taian, Shandong Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "scallion" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Sony RX100M5 camera, fixed" + ], + "collection_period": "2026", + "platform": "fixed", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16161775", + "citation": "Jin, Y., Zhai, K., Li, X., Kong, Y., Song, Y., Li, Q., Wang, G., & Guo, H. (2026). *Representative Scallion Posture Dataset for GOPD-YOLO* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.21823465", + "bibtex": "@article{Jin_2026, title={GOPD-YOLO: A Lightweight Oriented Object Detection Network for Real-Time Scallion Posture Recognition}, volume={16}, ISSN={2077-0472}, url={http://dx.doi.org/10.3390/agriculture16161775}, DOI={10.3390/agriculture16161775}, number={16}, journal={Agriculture}, publisher={MDPI AG}, author={Jin, Yajing and Zhai, Kejia and Li, Xue and Kong, Ying and Song, Yue and Li, Qingjiang and Wang, Guangming and Guo, Hongen}, year={2026}, month=Aug, pages={1775} }", + "data_size_bytes": 1988053374, + "num_files": 5, + "num_images": 500, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.21823465", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "spread_synthetic_forest_ground_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "synthetic", + "crop_types": [], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "simulated ground-level camera" + ], + "collection_period": "", + "platform": "", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.ecoinf.2025.103085", + "citation": "Feng, Z., She, Y., & Srinivasan, K. (2024). *SPREAD: A Large-scale, High-fidelity Synthetic Dataset for Multiple Forest Vision Tasks (Part I)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14515915", + "bibtex": "@article{Feng_2025, title={SPREAD: A large-scale, high-fidelity synthetic dataset for multiple forest vision tasks}, volume={87}, ISSN={1574-9541}, url={http://dx.doi.org/10.1016/j.ecoinf.2025.103085}, DOI={10.1016/j.ecoinf.2025.103085}, journal={Ecological Informatics}, publisher={Elsevier BV}, author={Feng, Zhengpeng and She, Yihang and Keshav, Srinivasan}, year={2025}, month=July, pages={103085} }", + "data_size_bytes": 47372991374, + "num_files": 12, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.14515915", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "treesatai_aerial_tree_species_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "", + "location": [ + "Lower Saxony, Germany" + ], + "lat_lon": [], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "20 cm colour-infrared aerial orthophotos", + "Sentinel-1", + "Sentinel-2" + ], + "collection_period": "", + "platform": "aircraft", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.5194/essd-15-681-2023", + "citation": "Schulz, C., Ahlswede, S., Gava, C., Helber, P., Bischke, B., Arias, F., Förster, M., Hees, J., Demir, B., & Kleinschmit, B. (2022). *TreeSatAI Benchmark Archive for Deep Learning in Forest Applications* (Version 1.0.1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.6780578", + "bibtex": "@article{Ahlswede_2023, title={TreeSatAI Benchmark Archive\n : a multi-sensor, multi-label dataset for tree species classification in remote sensing}, volume={15}, ISSN={1866-3516}, url={http://dx.doi.org/10.5194/essd-15-681-2023}, DOI={10.5194/essd-15-681-2023}, number={2}, journal={Earth System Science Data}, publisher={Copernicus GmbH}, author={Ahlswede, Steve and Schulz, Christian and Gava, Christiano and Helber, Patrick and Bischke, Benjamin and Förster, Michael and Arias, Florencia and Hees, Jörn and Demir, Begüm and Kleinschmit, Birgit}, year={2023}, month=Feb, pages={681–695} }", + "data_size_bytes": 16274111706, + "num_files": 27, + "num_images": 17785, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.6780578", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "pixelcroprobot_vegetable_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Vairão, Vila do Conde, Portugal" + ], + "lat_lon": [], + "country": "Portugal", + "environment": "greenhouse", + "real_or_synthetic": "real", + "crop_types": [ + "arugula", + "carrot", + "coriander", + "lettuce", + "radish", + "spinach", + "swiss chard", + "turnip" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Huawei Mate 10 Lite smartphone" + ], + "collection_period": "March-July 2021", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy13020463", + "citation": "Rodrigues, L., Terra, F., Magalhães, S., Santos, F., Moura, P., & Cunha, M. (2021). *PixelCropRobot dataset: images of vegetables crops in different phenological stages taken in greenhouses* (Version 1.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7433286", + "bibtex": "@article{Rodrigues_2023, title={Computer Vision and Deep Learning as Tools for Leveraging Dynamic Phenological Classification in Vegetable Crops}, volume={13}, ISSN={2073-4395}, url={http://dx.doi.org/10.3390/agronomy13020463}, DOI={10.3390/agronomy13020463}, number={2}, journal={Agronomy}, publisher={MDPI AG}, author={Rodrigues, Leandro and Magalhães, Sandro Augusto and da Silva, Daniel Queirós and dos Santos, Filipe Neves and Cunha, Mário}, year={2023}, month=Feb, pages={463} }", + "data_size_bytes": 28789930585, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7433286", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "cottonweeddet12_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": [ + "cotton fields in the southern United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "smartphones", + "hand-held digital cameras" + ], + "collection_period": "June-September 2021", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [], + "license": "cc-by-nc-4.0", + "documentation": "https://doi.org/10.1016/j.compag.2023.107655", + "citation": "Lu, Y. (2023). *CottonWeedDet12: a 12-class weed dataset of cotton production systems for benchmarking AI models for weed detection* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7535814", + "bibtex": "@article{Dang_2023, title={YOLOWeeds: A novel benchmark of YOLO object detectors for multi-class weed detection in cotton production systems}, volume={205}, ISSN={0168-1699}, url={http://dx.doi.org/10.1016/j.compag.2023.107655}, DOI={10.1016/j.compag.2023.107655}, journal={Computers and Electronics in Agriculture}, publisher={Elsevier BV}, author={Dang, Fengying and Chen, Dong and Lu, Yuzhen and Li, Zhaojian}, year={2023}, month=Feb, pages={107655} }", + "data_size_bytes": 28586468345, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7535814", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "rumexweeds_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": [ + "3 grassland farms (RumexWeeds, DTU)" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grassland" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "robot-mounted RGB camera (2.3 MP)" + ], + "collection_period": "summer-autumn 2021", + "platform": "robot", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [ + "rumex_obtusifolius", + "rumex_crispus" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1002/rob.22196", + "citation": "Güldenring, R., Li, J., van Evert, F., & Nalpantidis, L. (2023). *Data for \"RumexWeeds: A Grassland Dataset for Agricultural Robotics\"* [Dataset]. Technical University of Denmark. https://doi.org/10.11583/DTU.17040518.V2", + "bibtex": "@article{G_ldenring_2023, title={RumexWeeds: A grassland dataset for agricultural robotics}, volume={40}, ISSN={1556-4967}, url={http://dx.doi.org/10.1002/rob.22196}, DOI={10.1002/rob.22196}, number={6}, journal={Journal of Field Robotics}, publisher={Wiley}, author={Güldenring, Ronja and van Evert, Frits K. and Nalpantidis, Lazaros}, year={2023}, month=May, pages={1639–1656} }", + "data_size_bytes": 30210000000, + "num_files": 1, + "num_images": 5510, + "source": "doi", + "data_link": "https://doi.org/10.11583/DTU.17040518.v2", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "digipathos_pddb_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Brazil" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "black pepper", + "cabbage", + "cashew tree", + "cassava", + "citrus", + "coconut tree", + "coffee", + "corn", + "cotton", + "cupuacu", + "dry bean", + "grapevine", + "melon", + "oil palm", + "papaya", + "passion fruit", + "pineapple", + "rice", + "soybean", + "sugarcane", + "wheat" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-nc-4.0", + "documentation": "https://doi.org/10.1109/TLA.2018.8444395", + "citation": "Barbedo, J. G. A., Koenigkan, L. V., Halfeld-Vieira, B. A., Costa, R. V. D., Nechet, K. L., Godoy, C. V., Lobo Junior, M., Patricio, F. R. A., Talamini, V., Chitarra, L. G., Oliveira, S. A. S., Ishida, A. K. N., Fernandes, J. M. C., Santos, T. T., Cavalcanti, F. R., & Terao, D. (2024). *Base de Imagens de Sintomas de Doenças de Plantas PDDB.* [Dataset]. Redape. https://doi.org/10.48432/XA1OVL", + "bibtex": "@article{Garcia_Arnal_Barbedo_2018, title={Annotated Plant Pathology Databases for Image-Based Detection and Recognition of Diseases}, volume={16}, ISSN={1548-0992}, url={http://dx.doi.org/10.1109/TLA.2018.8444395}, DOI={10.1109/tla.2018.8444395}, number={6}, journal={IEEE Latin America Transactions}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Garcia Arnal Barbedo, Jayme and Vieira Koenigkan, Luciano and Almeida Halfeld-Vieira, Bernardo and Veras Costa, Rodrigo and Lima Nechet, Katia and Vieira Godoy, Claudia and Lobo Junior, Murillo and Rodrigues Alves Patricio, Flavia and Talamini, Viviane and Gonzaga Chitarra, Luiz and Alves Santos Oliveira, Saulo and Nakasone Ishida, Alessandra Keiko and Cunha Fernandes, Jose Mauricio and Teixeira Santos, Thiago and Rossi Cavalcanti, Fabio and Terao, Daniel and Angelotti, Francislene}, year={2018}, month=June, pages={1749–1757} }", + "data_size_bytes": 8320000000, + "num_files": 278, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.48432/XA1OVL", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "maize_stereo_matching_pairs", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [], + "lat_lon": [], + "country": "", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "stereo camera (left/right pairs, 1080x720 and 640x360)" + ], + "collection_period": "2023, 2025", + "platform": "", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-nc-4.0", + "documentation": "https://doi.org/10.3390/agriculture15242573", + "citation": "Torres, Í. A., Hernández-García, R., Valencia, J. S. B., Londoño, J. D. Z., & Vera, E. R. (2025). Stereo-Matching Deep Learning Dataset for Maize 3D Phenotyping. *OSF*. https://doi.org/10.17605/OSF.IO/MN6P9", + "bibtex": "@article{Zapata_Londo_o_2025, title={Automated 3D Phenotyping of Maize Plants: Stereo Matching Guided by Deep Learning}, volume={15}, ISSN={2077-0472}, url={http://dx.doi.org/10.3390/agriculture15242573}, DOI={10.3390/agriculture15242573}, number={24}, journal={Agriculture}, publisher={MDPI AG}, author={Zapata-Londoño, Juan and Botero-Valencia, Juan and Torres, Ítalo A. and Reyes-Vera, Erick and Hernández-García, Ruber}, year={2025}, month=Dec, pages={2573} }", + "data_size_bytes": 650000000, + "num_files": 543, + "num_images": 543, + "source": "doi", + "data_link": "https://doi.org/10.17605/OSF.IO/MN6P9", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sandplain_lupin_weed_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Mingenew, Western Australia, Australia" + ], + "lat_lon": [], + "country": "Australia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "narrow-leafed lupin", + "sandplain lupin (weed)" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Phantom 4 UAV RGB camera", + "smartphone" + ], + "collection_period": "", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15071817", + "citation": "Danilevicz, M., Rocha, R. L., Batley, J., Bayer, P. E., Bennamoun, M., Edwards, D., & Ashworth, M. (2023). *Segmentation of sandplain lupin weeds from morphologically similar narrow-leafed lupins in the field* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.21746669.V1", + "bibtex": "@article{Danilevicz_2023, title={Segmentation of Sandplain Lupin Weeds from Morphologically Similar Narrow-Leafed Lupins in the Field}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15071817}, DOI={10.3390/rs15071817}, number={7}, journal={Remote Sensing}, publisher={MDPI AG}, author={Danilevicz, Monica F. and Rocha, Roberto Lujan and Batley, Jacqueline and Bayer, Philipp E. and Bennamoun, Mohammed and Edwards, David and Ashworth, Michael B.}, year={2023}, month=Mar, pages={1817} }", + "data_size_bytes": 12842629481, + "num_files": 5, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.6084/m9.figshare.21746669.v1", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "grape_multimodal_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Hefei, Anhui, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "grape" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "RGB, depth and NIR camera" + ], + "collection_period": "August 2021 - July 2023", + "platform": "", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy16070679", + "citation": "Wenjun Chen, Rao, Y., Fengyi Wang, Zhang, Y., Yumeng Yang, Luo, Q., Zhang, T., Tianyu Wan, Xinyu Liu, Mengyu Zhang, & Zhang, R. (2023). *A dataset of grape multimodal object detection and semantic segmentation* (Version V1) [Dataset]. Science Data Bank. https://doi.org/10.57760/SCIENCEDB.J00001.00883", + "bibtex": "@article{Wang_2026, title={YOLOv11-WFD: A Multimodal Grape Segmentation Framework with Wavelet Convolution, FasterNeXt, and Dynamic Upsampling for Intelligent Harvesting}, volume={16}, ISSN={2073-4395}, url={http://dx.doi.org/10.3390/agronomy16070679}, DOI={10.3390/agronomy16070679}, number={7}, journal={Agronomy}, publisher={MDPI AG}, author={Wang, Pengyan and Li, Chengshuai and Wei, Linjing}, year={2026}, month=Mar, pages={679} }", + "data_size_bytes": 42000000000, + "num_files": 5, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.57760/sciencedb.j00001.00883", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tomato_multimodal_maturity_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "maturity_detection", + "location": [], + "lat_lon": [], + "country": "China", + "environment": "", + "real_or_synthetic": "real", + "crop_types": [ + "tomato" + ], + "sensor_modality": "rgb-d", + "imaging_equipment": [ + "RGB, depth and infrared camera" + ], + "collection_period": "", + "platform": "", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16101044", + "citation": "ZHANG Yu, RAO Yuan, CHEN Wenjun, HOU Wenhui, YAN Shengli, LI Yang, ZHOU Chuanqi, WANG Fengyi, CHU Youyi, & SHI Yulong. (2023). *Multimodal image dataset of tomato fruits with different maturity* (Version V1) [Dataset]. Science Data Bank. https://doi.org/10.57760/SCIENCEDB.J00001.00946", + "bibtex": "@article{Di_2026, title={DTNet: A Novel Infrared-Visible Fruit Object Detection Method Based on Dual-Modal Feature Interaction Fusion and Transformer Decoding}, volume={16}, ISSN={2077-0472}, url={http://dx.doi.org/10.3390/agriculture16101044}, DOI={10.3390/agriculture16101044}, number={10}, journal={Agriculture}, publisher={MDPI AG}, author={Di, Ziqian and Sun, Guoxiang}, year={2026}, month=May, pages={1044} }", + "data_size_bytes": 10830000000, + "num_files": 5, + "num_images": 3999, + "source": "doi", + "data_link": "https://doi.org/10.57760/sciencedb.j00001.00946", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "maize_in_field_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "South Africa" + ], + "lat_lon": [], + "country": "South Africa", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [ + "grey_leaf_spot", + "northern_corn_leaf_blight", + "common_rust", + "southern_rust", + "phaeosphaeria_leaf_spot" + ], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0322705", + "citation": "Hamish Craze, & Dave Berger. (2022). *Maize_in_Field_Dataset* [Dataset]. Kaggle. https://doi.org/10.34740/KAGGLE/DS/2086820", + "bibtex": "@article{Subbarayudu_2025, title={Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN}, volume={20}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0322705}, DOI={10.1371/journal.pone.0322705}, number={5}, journal={PLOS One}, publisher={Public Library of Science (PLoS)}, author={Subbarayudu, Chatla and Kubendiran, Mohan}, editor={Sun, Xiaoyong}, year={2025}, month=May, pages={e0322705} }", + "data_size_bytes": 10787457878, + "num_files": 0, + "num_images": 2355, + "source": "doi", + "data_link": "https://doi.org/10.34740/kaggle/ds/2086820", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tomato_greenhouse_label_errors_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Zempoala, Hidalgo, Mexico" + ], + "lat_lon": [ + "19.956306, -98.694694" + ], + "country": "Mexico", + "environment": "greenhouse", + "real_or_synthetic": "real", + "crop_types": [ + "tomato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "March-July 2023", + "platform": "", + "input_data_format": "external", + "annotation_format": "boundingBox", + "classes": [], + "license": "cdla-sharing-1.0", + "documentation": "https://doi.org/10.3390/agriculture15121291", + "citation": "Alvarez Hernandez, G. A. (2024). *Tomate detection with errors* [Dataset]. Kaggle. https://www.kaggle.com/datasets/gerardoantony/tomate-detection-with-errors", + "bibtex": "@article{Zamora_Su_rez_2025, title={Automatic Correction of Labeling Errors Applied to Tomato Detection}, volume={15}, ISSN={2077-0472}, url={http://dx.doi.org/10.3390/agriculture15121291}, DOI={10.3390/agriculture15121291}, number={12}, journal={Agriculture}, publisher={MDPI AG}, author={Zamora Suárez, Ángel Eduardo and Alvarez Hernandez, Gerardo Antonio and Vasquez, Juan Irving and Taud, Hind and Uriarte-Arcia, Abril Valeria and Zamora, Erik}, year={2025}, month=June, pages={1291} }", + "data_size_bytes": 3164280220, + "num_files": 0, + "num_images": 2000, + "source": "doi", + "data_link": "https://www.kaggle.com/datasets/gerardoantony/tomate-detection-with-errors", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "agripk_whitefly_cotton_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "damage_classification", + "location": [ + "Bahawalpur, South Punjab, Pakistan" + ], + "lat_lon": [], + "country": "Pakistan", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "mobile phones", + "DSLR cameras (5-12 MP)" + ], + "collection_period": "August-October 2021", + "platform": "handheld", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture12101529", + "citation": "Jajja, A. I. (2021). *AgriPk dataset for Whitefly affected leaves* [Dataset]. Kaggle. https://www.kaggle.com/datasets/aqeeljajja/whitefly-leave-classification", + "bibtex": "@article{Jajja_2022, title={Compact Convolutional Transformer (CCT)-Based Approach for Whitefly Attack Detection in Cotton Crops}, volume={12}, ISSN={2077-0472}, url={http://dx.doi.org/10.3390/agriculture12101529}, DOI={10.3390/agriculture12101529}, number={10}, journal={Agriculture}, publisher={MDPI AG}, author={Jajja, Aqeel Iftikhar and Abbas, Assad and Khattak, Hasan Ali and Niedbała, Gniewko and Khalid, Abbas and Rauf, Hafiz Tayyab and Kujawa, Sebastian}, year={2022}, month=Sept, pages={1529} }", + "data_size_bytes": 13003216408, + "num_files": 0, + "num_images": 5137, + "source": "doi", + "data_link": "https://www.kaggle.com/datasets/aqeeljajja/whitefly-leave-classification", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "wald5dplus_forest_sentinel_cubes", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Bavarian Forest National Park, Germany", + "Kranzberg Forest, Germany", + "Steigerforest (as named in the data), Germany" + ], + "lat_lon": [], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1 SAR", + "Sentinel-2 MSI" + ], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17071246", + "citation": "Hauser, S., Schmitt, A., Krzystek, P., & Ruhhammer, M. (2024). *Wald5Dplus* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10848838", + "bibtex": "@article{Hauser_2025, title={Perfect Labelling: A Review and Outlook of Label Optimization Techniques in Dynamic Earth Observation}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17071246}, DOI={10.3390/rs17071246}, number={7}, journal={Remote Sensing}, publisher={MDPI AG}, author={Hauser, Sarah and Augner, Lena and Schmitt, Andreas}, year={2025}, month=Apr, pages={1246} }", + "data_size_bytes": 31634803629, + "num_files": 1, + "num_images": 3114, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10848838", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "esa_worldcover_2020_land_cover", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_classification", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1 SAR", + "Sentinel-2 MSI" + ], + "collection_period": "2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [ + "tree cover", + "shrubland", + "grassland", + "cropland", + "built-up", + "bare / sparse vegetation", + "snow and ice", + "permanent water bodies", + "herbaceous wetland", + "mangroves", + "moss and lichen" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17081377", + "citation": "Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., Vergnaud, S., Cartus, O., Santoro, M., Fritz, S., Georgieva, I., Lesiv, M., Carter, S., Herold, M., Li, L., Tsendbazar, N.-E., … Arino, O. (2021). *ESA WorldCover 10 m 2020 v100* (Version v100) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5571936", + "bibtex": "@article{Huang_2025, title={Global Distribution and Local Variation of Pre-Rain Green-Up in Tropical Dryland}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17081377}, DOI={10.3390/rs17081377}, number={8}, journal={Remote Sensing}, publisher={MDPI AG}, author={Huang, Shuyi and Sang, Yirong and Cai, Zhanzhang and Tian, Feng}, year={2025}, month=Apr, pages={1377} }", + "data_size_bytes": 120064234637, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.5571936", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "umbellularia_resprouting_tls", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Pepperwood Preserve, Sonoma County, California, United States", + "Saddle Mountain, Sonoma County, California, United States" + ], + "lat_lon": [ + "38.582639, -122.710361", + "38.500917, -122.629056" + ], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Umbellularia californica (California bay laurel)" + ], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs17173101", + "citation": "Bell, D., Smeds, E., & Bentley, L. P. (2025). *Data from: Assessing Umbellularia californica basal resprouting response post-wildfire using field measurements and ground-based LiDAR scanning* (Version 1.0.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.17049872", + "bibtex": "@article{Bell_2025, title={Assessing Umbellularia californica Basal Resprouting Response Post-Wildfire Using Field Measurements and Ground-Based LiDAR Scanning}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17173101}, DOI={10.3390/rs17173101}, number={17}, journal={Remote Sensing}, publisher={MDPI AG}, author={Bell, Dawson and Halbur, Michelle and Elias, Francisco and Pearson, Nancy and Crocker, Daniel E. and Bentley, Lisa Patrick}, year={2025}, month=Sept, pages={3101} }", + "data_size_bytes": 1122605779, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.17049872", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "cfatd_china_forest_biomass", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18152639", + "citation": "Cai, Y., Zhu, P., Xu, X., Li, X., Zhang, H., Nie, S., Wang, C., Wang, J., Shen, Q., Li, B., Wu, C., Liu, X., & Chen, Y. (2024). *CFATD: The First High-Spatiotemporal-Resolution Mapping of Forest Aboveground Biomass in China from 1985 to 2023 (Part Ⅵ: 2022-2023)* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.12747329", + "bibtex": "@article{Luo_2026, title={Spatial Pattern of Extreme Rainfall-Induced Forest Aboveground Biomass Loss and Its Influencing Factors in the Tableland-Gully Region of the Loess Plateau, China}, volume={18}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs18152639}, DOI={10.3390/rs18152639}, number={15}, journal={Remote Sensing}, publisher={MDPI AG}, author={Luo, Xiaoqing and Li, Yayi and Yang, Menghan and Zhang, Yuhang and Wang, Jiaxi and Li, Mengmeng and Deng, Runqiu and Yang, Feng and Jiao, Juying}, year={2026}, month=Aug, pages={2639} }", + "data_size_bytes": 15224798858, + "num_files": 2, + "num_images": 2, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.12747329", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "for_instance_uav_lidar_tree_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Norway", + "Czech Republic", + "Austria", + "New Zealand", + "Australia" + ], + "lat_lon": [], + "country": "Norway; Czech Republic; Austria; New Zealand; Australia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "UAV laser scanner" + ], + "collection_period": "", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "pointLabels", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18162697", + "citation": "Puliti, S., Pearse, G., Surový, P., Wallace, L., Hollaus, M., Wielgosz, M., & Astrup, R. (2023). *FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees* (Version 1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.8287792", + "bibtex": "@article{Liu_2026, title={PineSegNet: A Deep Learning Method for Fine-Grained Wood-Leaf Segmentation of Masson Pine Point Clouds}, volume={18}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs18162697}, DOI={10.3390/rs18162697}, number={16}, journal={Remote Sensing}, publisher={MDPI AG}, author={Liu, Yaxue and Li, Lexiang and Zhang, Xiaogang and Liu, Shuai and Sun, Hua}, year={2026}, month=Aug, pages={2697} }", + "data_size_bytes": 1643251814, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.8287792", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "global_natural_planted_forests_30m", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [ + "natural forest", + "planted forest" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18010170", + "citation": "Xiao, Y. (2024). *Global Natural and Planted Forests Mapping at Fine Spatial Resolution of 30 m* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10701417", + "bibtex": "@article{Wei_2026, title={Exploring the Roles of Ancient Trees in Disturbance and Recovery Processes Using Monthly Landsat Time Series Analysis}, volume={18}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs18010170}, DOI={10.3390/rs18010170}, number={1}, journal={Remote Sensing}, publisher={MDPI AG}, author={Wei, Yutong and Sun, Lin and Jia, Jingyi and Meng, Yuanyuan and Zhang, Junwei and Zhou, Xin and Xie, Jiaxuan and Yang, Jun and Huang, Li}, year={2026}, month=Jan, pages={170} }", + "data_size_bytes": 19643752616, + "num_files": 10, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10701417", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "sen12tp_sentinel_paired_vegetation_indices", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1 SAR", + "Sentinel-2 MSI" + ], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18142400", + "citation": "Roßberg, T., & Schmitt, M. (2023). *SEN12TP - Sentinel-1 and -2 images, timely paired* (Version v1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7342060", + "bibtex": "@article{Barbato_2026, title={On the Use of SAR Images for Predicting Vegetation Indices: Challenges and Limitations}, volume={18}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs18142400}, DOI={10.3390/rs18142400}, number={14}, journal={Remote Sensing}, publisher={MDPI AG}, author={Barbato, Mirko Paolo and Cilli, Roberto and Napoletano, Paolo and Pompili, Alexis and Ramirez-Sanchez, Gabriel and Sozbilir, Umit}, year={2026}, month=July, pages={2400} }", + "data_size_bytes": 237924431459, + "num_files": 8, + "num_images": 8380, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7342060", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "global_mangrove_watch_v3", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "mangrove" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "1996-2020", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [ + "mangrove" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18172975", + "citation": "Bunting, P., Rosenqvist, A., Hilarides, L., Lucas, R., Thomas, N., Tadono, T., Worthington, T., Spalding, M., Murray, N., & Rebelo, L.-M. (2022). *Global Mangrove Watch (1996 - 2020) Version 3.0 Dataset* (Version 3.0) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.6894273", + "bibtex": "@article{Qian_2026, title={Controlled Evaluation of Sentinel-2 Annual Compositing Strategies for Deep Learning-Based Mangrove Mapping in China}, volume={18}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs18172975}, DOI={10.3390/rs18172975}, number={17}, journal={Remote Sensing}, publisher={MDPI AG}, author={Qian, Jun and Wang, Xuewu and Dai, Fangyu and Qiu, Zixuan}, year={2026}, month=Sept, pages={2975} }", + "data_size_bytes": 5286160794, + "num_files": 51, + "num_images": 3042, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.6894273", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "alberta_forest_rgb_thermal_drone", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Cynthia, Alberta, Canada" + ], + "lat_lon": [], + "country": "Canada", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "thermal-rgb", + "imaging_equipment": [ + "drone RGB camera", + "drone thermal camera" + ], + "collection_period": "30 August 2022", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15102653", + "citation": "Rudraksh Kapil, Castilla, G., Marvasti-Zadeh, S. M., Goodsman, D., Erbilgin, N., & Nilanjan Ray. (2023). *RGB and Thermal Drone Imagery over Forest Stand in Alberta, Canada* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7662405", + "bibtex": "@article{Kapil_2023, title={Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15102653}, DOI={10.3390/rs15102653}, number={10}, journal={Remote Sensing}, publisher={MDPI AG}, author={Kapil, Rudraksh and Castilla, Guillermo and Marvasti-Zadeh, Seyed Mojtaba and Goodsman, Devin and Erbilgin, Nadir and Ray, Nilanjan}, year={2023}, month=May, pages={2653} }", + "data_size_bytes": 9448386047, + "num_files": 1, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7662405", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "agrisen_cog_crop_type_benchmark", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "crop_classification", + "location": [ + "multiple European countries (LPIS)" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-2 MSI" + ], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15122980", + "citation": "Selea, T. (2023). *AgriSen-COG.LPIS.GT.* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7892012", + "bibtex": "@article{Selea_2023, title={AgriSen-COG, a Multicountry, Multitemporal Large-Scale Sentinel-2 Benchmark Dataset for Crop Mapping Using Deep Learning}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15122980}, DOI={10.3390/rs15122980}, number={12}, journal={Remote Sensing}, publisher={MDPI AG}, author={Selea, Teodora}, year={2023}, month=June, pages={2980} }", + "data_size_bytes": 28175787944, + "num_files": 9, + "num_images": 248, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7892012", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "cgls_lc100_2019_land_cover", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_classification", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "2019", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15235594", + "citation": "Buchhorn, M., Smets, B., Bertels, L., Roo, B. D., Lesiv, M., Tsendbazar, N.-E., Herold, M., & Fritz, S. (2020). *Copernicus Global Land Service: Land Cover 100m: collection 3: epoch 2019: Globe* (Version V3.0.1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.3939050", + "bibtex": "@article{Sipps_2023, title={Modeling Uncertainty of GEDI Clear-Sky Terrain Height Retrievals Using a Mixture Density Network}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15235594}, DOI={10.3390/rs15235594}, number={23}, journal={Remote Sensing}, publisher={MDPI AG}, author={Sipps, Jonathan and Magruder, Lori A.}, year={2023}, month=Dec, pages={5594} }", + "data_size_bytes": 41207135373, + "num_files": 16, + "num_images": 16, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.3939050", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "tls_forest_pulse_frequency", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Belgium", + "Cumberland Plain SuperSite, New South Wales, Australia", + "Gabon" + ], + "lat_lon": [ + "50.966667, 3.800000", + "-33.600000, 150.716667", + "0.500000, 12.783333" + ], + "country": "Belgium; Australia; Gabon", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "January-June 2022", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16234560", + "citation": "Tom, V., Calders, K., Burt, A., Demol, M., D'hont, B., Nightingale, J., Terryn, L., & Verbeeck, H. (2024). *TLS data from 'Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics'* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10671784", + "bibtex": "@article{Verhelst_2024, title={Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16234560}, DOI={10.3390/rs16234560}, number={23}, journal={Remote Sensing}, publisher={MDPI AG}, author={Verhelst, Tom E. and Calders, Kim and Burt, Andrew and Demol, Miro and D’hont, Barbara and Nightingale, Joanne and Terryn, Louise and Verbeeck, Hans}, year={2024}, month=Dec, pages={4560} }", + "data_size_bytes": 7704594188, + "num_files": 6, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.10671784", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "eba_amazon_airborne_lidar_transects", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Brazilian Amazon" + ], + "lat_lon": [], + "country": "Brazil", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "airborne LiDAR" + ], + "collection_period": "", + "platform": "aircraft", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16213935", + "citation": "Ometto, J., Gorgens, E. B., Pereira, F. R. de S., Sato, L., Assis, M. L. R., Cantinho, R., Longo, M., Jacon, A. D., & Keller, M. (2023). *L1A - Discrete airborne LiDAR transects collected by EBA in the Brazilian Amazon (Mato Grosso, Amazonas e Pará)* (Version v20230303) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.7636454", + "bibtex": "@article{Almeida_2024, title={Advancing Forest Degradation and Regeneration Assessment Through Light Detection and Ranging and Hyperspectral Imaging Integration}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16213935}, DOI={10.3390/rs16213935}, number={21}, journal={Remote Sensing}, publisher={MDPI AG}, author={Almeida, Catherine Torres de and Galvão, Lênio Soares and Ometto, Jean Pierre H. B. and Jacon, Aline Daniele and Pereira, Francisca Rocha de Souza and Sato, Luciane Yumie and Silva-Junior, Celso Henrique Leite and Brancalion, Pedro H. S. and Aragão, Luiz Eduardo Oliveira e Cruz de}, year={2024}, month=Oct, pages={3935} }", + "data_size_bytes": 160211304846, + "num_files": 11, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.7636454", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "crop_classification_planetscope_sentinel2", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "Harichand, Khyber Pakhtunkhwa, Pakistan" + ], + "lat_lon": [], + "country": "Pakistan", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "PlanetScope", + "Sentinel-2" + ], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "classLabel", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0271897", + "citation": "Khan, W., Ahmad, T., Touseef Ur Rehman, Maaz Alam, & Nasru Minallah. (2022). *Crop Classification Data-set* (Version 1) [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.6560241", + "bibtex": "@article{Rehman_2023, title={Long short term memory deep net performance on fused Planet-Scope and Sentinel-2 imagery for detection of agricultural crop}, volume={18}, ISSN={1932-6203}, url={http://dx.doi.org/10.1371/journal.pone.0271897}, DOI={10.1371/journal.pone.0271897}, number={2}, journal={PLOS ONE}, publisher={Public Library of Science (PLoS)}, author={Rehman, Touseef Ur and Alam, Maaz and Minallah, Nasru and Khan, Waleed and Frnda, Jaroslav and Mushtaq, Shawal and Ajmal, Muhammad}, editor={Kumar, Manoj}, year={2023}, month=Feb, pages={e0271897} }", + "data_size_bytes": 495135776, + "num_files": 3, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.6560241", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "glass_glc_global_land_cover_1982_2015", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_classification", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "AVHRR (GLASS climate data records)" + ], + "collection_period": "1982-2015", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [ + "cropland", + "forest", + "grassland", + "shrubland", + "tundra", + "barren land", + "snow/ice" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15215134", + "citation": "Liu, H., Gong, P., Wang, J., Clinton, N., Bai, Y., & Liang, S. (2020). *Annual dynamics of global land cover and its long-term changes from 1982 to 2015, link to GeoTIFF files* [Dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.913496", + "bibtex": "@article{Cui_2023, title={Comparison and Assessment of Different Land Cover Datasets on the Cropland in Northeast China}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15215134}, DOI={10.3390/rs15215134}, number={21}, journal={Remote Sensing}, publisher={MDPI AG}, author={Cui, Peipei and Chen, Tan and Li, Yingjie and Liu, Kai and Zhang, Dapeng and Song, Chunqiao}, year={2023}, month=Oct, pages={5134} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.1594/pangaea.913496", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "central_europe_forest_tls_uls_als_point_clouds", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Bretten and Karlsruhe, Baden-Württemberg, Germany" + ], + "lat_lon": [ + "49.034897, 8.426119", + "49.013789, 8.694831" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner", + "UAV-borne laser scanner", + "airborne laser scanner" + ], + "collection_period": "2019-2020", + "platform": "mixed", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-sa-4.0", + "documentation": "https://doi.org/10.3390/rs17071304", + "citation": "Weiser, H., Schäfer, J., Winiwarter, L., Krašovec, N., Seitz, C., Schimka, M., Anders, K., Baete, D., Braz, A. S., Brand, J., Debroize, D., Kuss, P., Martin, L. L., Mayer, A., Schrempp, T., Schwarz, L.-M., Ulrich, V., Fassnacht, F. E., & Höfle, B. (2022). *Terrestrial, UAV-borne, and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements* [Dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.942856", + "bibtex": "@article{Vahrenhold_2025, title={MMTSCNet: Multimodal Tree Species Classification Network for Classification of Multi-Source, Single-Tree LiDAR Point Clouds}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17071304}, DOI={10.3390/rs17071304}, number={7}, journal={Remote Sensing}, publisher={MDPI AG}, author={Vahrenhold, Jan Richard and Brandmeier, Melanie and Müller, Markus Sebastian}, year={2025}, month=Apr, pages={1304} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.1594/pangaea.942856", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "central_europe_forest_als_full_waveform", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Bretten and Karlsruhe, Baden-Württemberg, Germany" + ], + "lat_lon": [ + "49.034897, 8.426119", + "49.013789, 8.694831" + ], + "country": "Germany", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "full-waveform airborne laser scanner" + ], + "collection_period": "July 2019", + "platform": "aircraft", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-sa-4.0", + "documentation": "https://doi.org/10.3390/rs17071304", + "citation": "Weiser, H., Winiwarter, L., Schäfer, J., Fassnacht, F. E., & Höfle, B. (2022). *Airborne laser scanning (ALS) point clouds with full-waveform (FWF) data of central European forest plots, Germany* [Dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.947038", + "bibtex": "@article{Vahrenhold_2025, title={MMTSCNet: Multimodal Tree Species Classification Network for Classification of Multi-Source, Single-Tree LiDAR Point Clouds}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17071304}, DOI={10.3390/rs17071304}, number={7}, journal={Remote Sensing}, publisher={MDPI AG}, author={Vahrenhold, Jan Richard and Brandmeier, Melanie and Müller, Markus Sebastian}, year={2025}, month=Apr, pages={1304} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.1594/pangaea.947038", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "rushworth_forest_tls_tree_point_clouds", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Rushworth Forest, Victoria, Australia" + ], + "lat_lon": [], + "country": "Australia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16183355", + "citation": "Calders. (2014). *Terrestrial laser scans - Riegl VZ400, individual tree point clouds and cylinder models, Rushworth Forest* [Dataset]. Terrestrial Ecosystem Research Network (TERN). https://doi.org/10.4227/05/542B766D5D00D", + "bibtex": "@article{Tang_2024, title={Cluster-Based Wood–Leaf Separation Method for Forest Plots Using Terrestrial Laser Scanning Data}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16183355}, DOI={10.3390/rs16183355}, number={18}, journal={Remote Sensing}, publisher={MDPI AG}, author={Tang, Hao and Li, Shihua and Su, Zhonghua and He, Ze}, year={2024}, month=Sept, pages={3355} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.4227/05/542b766d5d00d", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "obsydya_benin_land_cover_reference", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "land_use_classification", + "location": [ + "Central and Northern Benin" + ], + "lat_lon": [], + "country": "Benin", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "vectorPolygons", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18152622", + "citation": "GAETANO, R., & BIO NIKKI, B. (2026). *Land Cover Reference Data Series, OBSYDYA project, Benin* [Dataset]. CIRAD. https://doi.org/10.18167/GEODE/9446", + "bibtex": "@article{Bio_Nikki_Sar__2026, title={MSF-Net: A Multimodal SAR–Optical Fusion Network for Agricultural Land Use Classification in Smallholder Landscapes of Northern Benin}, volume={18}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs18152622}, DOI={10.3390/rs18152622}, number={15}, journal={Remote Sensing}, publisher={MDPI AG}, author={Bio Nikki Sarè, Sabi Bruno and Gaetano, Raffaele and Hountondji, Yvon-Carmen and Interdonato, Roberto}, year={2026}, month=Aug, pages={2622} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.18167/geode/9446", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "jasper_mountain_pine_beetle_tls_fuel", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "Jasper National Park, Alberta, Canada" + ], + "lat_lon": [], + "country": "Canada", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "July 2021", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "pointLabels", + "classes": [ + "stems", + "branches", + "foliage", + "downed woody logs", + "sapling stems", + "below-canopy branches", + "grass layer", + "ground" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15194778", + "citation": "Xi, Z., Hopkinson, C., & Chasmer, L. (2023). *Delineation and reconstruction of 3D components and volumes from mountain pine beetle-infected forest plots using terrestrial laser scanning (TLS)* [Dataset]. Federated Research Data Repository / dépôt fédéré de données de recherche. https://doi.org/10.20383/103.0814", + "bibtex": "@article{Xi_2023, title={Delineating and Reconstructing 3D Forest Fuel Components and Volumes with Terrestrial Laser Scanning}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15194778}, DOI={10.3390/rs15194778}, number={19}, journal={Remote Sensing}, publisher={MDPI AG}, author={Xi, Zhouxin and Chasmer, Laura and Hopkinson, Chris}, year={2023}, month=Sept, pages={4778} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.20383/103.0814", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "large_tropical_trees_tls_biomass_4tu", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Guyana", + "Central Kalimantan, Indonesia", + "Madre de Dios, Peru" + ], + "lat_lon": [ + "6.04, -58.70", + "-2.41, 113.13", + "-12.27, -69.10" + ], + "country": "Guyana; Indonesia; Peru", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs15205005", + "citation": "Gonzales de Tanago Menaca, J., Lau Sarmiento, A., Bartholomeus, H., Herold, M., Avitabile, V., Raumonen, P., Martius, C., Goodman, R. C., Disney, M., Manuri, S., Burt, A., & Calders, K. (2022). *Data underlying the publication: \"Estimation of above‐ground biomass of large tropical trees with terrestrial LiDAR\"* (Version 1) [Dataset]. 4TU.ResearchData. https://doi.org/10.4121/21552084", + "bibtex": "@article{Sun_2023, title={Assessing the Allometric Scaling of Vectorized Branch Lengths of Trees with Terrestrial Laser Scanning and Quantitative Structure Modeling: A Case Study in Guyana}, volume={15}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs15205005}, DOI={10.3390/rs15205005}, number={20}, journal={Remote Sensing}, publisher={MDPI AG}, author={Sun, Jingjing and Lin, Yi}, year={2023}, month=Oct, pages={5005} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.4121/21552084", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "cameroon_tropical_trees_tls_biomass", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "eastern Cameroon" + ], + "lat_lon": [], + "country": "Cameroon", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs16061021", + "citation": "Momo Takoudjou, S., Ploton, P., Sonké, B., Hackenberg, J., Griffon, S., de Coligny, F., Kamdem, N. G., Libalah, M., Mofack, G., Le Moguédec, G., Pélissier, R., & Barbier, N. (2018). *Data from: Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach* (Version 1) [Dataset]. Dryad. https://doi.org/10.5061/DRYAD.10HQ7", + "bibtex": "@article{Ali_2024, title={Benchmarking Geometry-Based Leaf-Filtering Algorithms for Tree Volume Estimation Using Terrestrial LiDAR Scanners}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16061021}, DOI={10.3390/rs16061021}, number={6}, journal={Remote Sensing}, publisher={MDPI AG}, author={Ali, Moonis and Lohani, Bharat and Hollaus, Markus and Pfeifer, Norbert}, year={2024}, month=Mar, pages={1021} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5061/dryad.10hq7", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "assateague_phragmites_uas_multispectral", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Assateague Island National Seashore, Maryland, United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Phragmites australis" + ], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "MicaSense Altum-PT", + "Ricoh GRII" + ], + "collection_period": "October 2022", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs16244691", + "citation": "Jennifer M Cramer, Alexandra D Evans, Sandra M Brosnahan, Jin-Si R Over, Seth Ackerman, Erika Lentz, & Bill Hulslander. (2023). *Topographic and multispectral reflectance products, aerial imagery, spectral reflectance profiles, vegetation surveys, and associated GPS data collected during uncrewed aircraft system (UAS) operations: Assateague Island National Seashore, Maryland, October 10-14, 2022* [Dataset]. U.S. Geological Survey. https://doi.org/10.5066/P916LRZN", + "bibtex": "@article{Evans_2024, title={Pragmatically Mapping Phragmites with Unoccupied Aerial Systems: A Comparison of Invasive Species Land Cover Classification Using RGB and Multispectral Imagery}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16244691}, DOI={10.3390/rs16244691}, number={24}, journal={Remote Sensing}, publisher={MDPI AG}, author={Evans, Alexandra Danielle and Cramer, Jennifer and Scholl, Victoria and Lentz, Erika}, year={2024}, month=Dec, pages={4691} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5066/p916lrzn", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "dog_head_marsh_uas_multispectral_spectra", + "machine_learning_task": "unlabeled", + "agricultural_task": "", + "location": [ + "Dog Head Marsh, South Cape Beach, Mashpee, Massachusetts, United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "UAS multispectral camera", + "OceanInsight HDX Mini spectrometer", + "ASD FieldSpec" + ], + "collection_period": "October 2021", + "platform": "uav", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/rs16244691", + "citation": "Jennifer M Cramer, Victoria M Scholl, Alexandra D Evans, Seth Ackerman, Elizabeth P Pendleton, Sandra M Brosnahan, Sydney K Nick, & Allyson A Boggess. (2024). *Topographic and multispectral reflectance products, aerial imagery, ground spectra, vegetation, and associated GPS data collected during uncrewed aircraft system operations - Dog Head Marsh at South Cape Beach, Mashpee, MA, October 7-8, 2021* [Dataset]. U.S. Geological Survey. https://doi.org/10.5066/P9W8JS7P", + "bibtex": "@article{Evans_2024, title={Pragmatically Mapping Phragmites with Unoccupied Aerial Systems: A Comparison of Invasive Species Land Cover Classification Using RGB and Multispectral Imagery}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16244691}, DOI={10.3390/rs16244691}, number={24}, journal={Remote Sensing}, publisher={MDPI AG}, author={Evans, Alexandra Danielle and Cramer, Jennifer and Scholl, Victoria and Lentz, Erika}, year={2024}, month=Dec, pages={4691} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.5066/p9w8js7p", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "globbiomass_global_forest_biomass", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "global" + ], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [], + "collection_period": "2010", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16060978", + "citation": "Santoro, M. (2018). *GlobBiomass - global datasets of forest biomass* [Dataset]. PANGAEA. https://doi.org/10.1594/PANGAEA.894711", + "bibtex": "@article{Liu_2024, title={Characterizing the Accelerated Global Carbon Emissions from Forest Loss during 1985–2020 Using Fine-Resolution Remote Sensing Datasets}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16060978}, DOI={10.3390/rs16060978}, number={6}, journal={Remote Sensing}, publisher={MDPI AG}, author={Liu, Wendi and Zhang, Xiao and Xu, Hong and Zhao, Tingting and Wang, Jinqing and Li, Zhehua and Liu, Liangyun}, year={2024}, month=Mar, pages={978} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.1594/pangaea.894711", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "hzau_field_rapeseed_point_clouds", + "machine_learning_task": "unlabeled", + "agricultural_task": "crop_phenotyping", + "location": [ + "Huazhong Agricultural University experimental field, Wuhan, Hubei, China" + ], + "lat_lon": [ + "30.4741, 114.3666" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "rapeseed" + ], + "sensor_modality": "point_cloud", + "imaging_equipment": [ + "terrestrial laser scanner" + ], + "collection_period": "", + "platform": "tripod", + "input_data_format": "external", + "annotation_format": "none", + "classes": [], + "license": "cc-by-nc-nd-4.0", + "documentation": "https://doi.org/10.3390/agronomy12102409", + "citation": "Fangzheng Hu. (2022). *Field rapeseed point clouds: Rapeseed-01* (Version V1) [Dataset]. Science Data Bank. https://doi.org/10.57760/SCIENCEDB.02204", + "bibtex": "@article{Hu_2022, title={Rapeseed Leaf Estimation Methods at Field Scale by Using Terrestrial LiDAR Point Cloud}, volume={12}, ISSN={2073-4395}, url={http://dx.doi.org/10.3390/agronomy12102409}, DOI={10.3390/agronomy12102409}, number={10}, journal={Agronomy}, publisher={MDPI AG}, author={Hu, Fangzheng and Lin, Chengda and Peng, Junwen and Wang, Jing and Zhai, Ruifang}, year={2022}, month=Oct, pages={2409} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.57760/sciencedb.02204", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "conghua_forest_agb_map", + "machine_learning_task": "ground_truth", + "agricultural_task": "", + "location": [ + "Conghua, Guangdong, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "multispectral", + "imaging_equipment": [ + "Sentinel-1 SAR", + "Sentinel-2 MSI", + "airborne LiDAR" + ], + "collection_period": "", + "platform": "satellite", + "input_data_format": "external", + "annotation_format": "numericMeasurements", + "classes": [], + "license": "cc-by-nc-sa-4.0", + "documentation": "https://doi.org/10.3390/rs17071285", + "citation": "Linjing Zhang, & Wenhao Jiang. (2025). *The Forest Aboveground Biomass Retrieval Result in Conghua,Guangdong* (Version V1) [Dataset]. Science Data Bank. https://doi.org/10.57760/SCIENCEDB.21081", + "bibtex": "@article{Jiang_2025, title={Multi-Decision Vector Fusion Model for Enhanced Mapping of Aboveground Biomass in Subtropical Forests Integrating Sentinel-1, Sentinel-2, and Airborne LiDAR Data}, volume={17}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs17071285}, DOI={10.3390/rs17071285}, number={7}, journal={Remote Sensing}, publisher={MDPI AG}, author={Jiang, Wenhao and Zhang, Linjing and Zhang, Xiaoxue and Gao, Si and Gao, Huimin and Sun, Lin and Yan, Guangjian}, year={2025}, month=Apr, pages={1285} }", + "data_size_bytes": 0, + "num_files": 0, + "num_images": 0, + "source": "doi", + "data_link": "https://doi.org/10.57760/sciencedb.21081", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "muufl_gulfport_hyperspectral_lidar", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "", + "location": [ + "University of Southern Mississippi Gulf Park campus, Long Beach, Mississippi, United States" + ], + "lat_lon": [], + "country": "United States", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [], + "sensor_modality": "hyperspectral", + "imaging_equipment": [ + "CASI-1500 hyperspectral imager", + "Optech Gemini airborne LiDAR" + ], + "collection_period": "November 2010", + "platform": "aircraft", + "input_data_format": "external", + "annotation_format": "segmentationMask", + "classes": [], + "license": "other", + "documentation": "https://doi.org/10.3390/rs16163082", + "citation": "Zare, A., Gader, P., Aitken, J., Close, R., Tuell, G., Glenn, T., Dranishnikov, D., & Xiaoxiao Du. (2018). *Gatorsense/Muuflgulfport: Release 01* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.1186326", + "bibtex": "@article{Wang_2024, title={Multimodal Semantic Collaborative Classification for Hyperspectral Images and LiDAR Data}, volume={16}, ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs16163082}, DOI={10.3390/rs16163082}, number={16}, journal={Remote Sensing}, publisher={MDPI AG}, author={Wang, Aili and Dai, Shiyu and Wu, Haibin and Iwahori, Yuji}, year={2024}, month=Aug, pages={3082} }", + "data_size_bytes": 406632079, + "num_files": 1, + "num_images": 301, + "source": "doi", + "data_link": "https://doi.org/10.5281/zenodo.1186326", + "examples_image_url": "", + "hosted_in_original_form": true + }, + { + "name": "Greenhouse_Tomato_AgRob_object_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Barroselas, Viana do Castelo, Portugal" + ], + "lat_lon": [], + "country": "Portugal", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "tomato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Stereolabs ZED stereo camera (RGB frames, 1280 x 720 px) on the AgRob v16 mobile robot, guided through greenhouse inter-rows" + ], + "collection_period": "6 and 8 August 2020", + "platform": "robot", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 449, + "classes": [ + "unripe", + "breaking", + "reddish", + "ripe" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy12020356", + "citation": "Magalhães, S. A., Moreira, G., dos Santos, F. N., & Cunha, M. (2021). *AgRobTomato Dataset: Greenhouse tomatoes with different ripeness stages* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.5596799", + "bibtex": "@article{moreira2022benchmark,\n title={Benchmark of Deep Learning and a Proposed HSV Colour Space Models for the Detection and Classification of Greenhouse Tomato},\n author={Moreira, Germano and Magalhães, Sandro Augusto and Pinho, Tatiana and dos Santos, Filipe Neves and Cunha, Mário},\n journal={Agronomy},\n volume={12},\n pages={356},\n year={2022},\n publisher={MDPI}\n}", + "zip_size_bytes": 139036606, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/Greenhouse_Tomato_AgRob_object_detection", + "examples_image_url": "/img/agml/sample_images/Greenhouse_Tomato_AgRob_object_detection_sample.webp" + }, + { + "name": "sugar_beet_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": [ + "Sokerijuurikkaan Tutkimuslaitos, Hevonpää, Paimio, Finland" + ], + "lat_lon": [], + "country": "Finland", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "sugar beet" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "GoPro Hero 11 action camera (5568 x 4872 px) on a 2 m boom mounted on a FarmDroid FD20 seeding/weeding robot, ~1380 px/m", + "Parrot Anafi drone RGB camera (4608 x 3456 px) flown at 4 m, ~570 px/m", + "Mobile phone camera (handheld)" + ], + "collection_period": "May-July 2023", + "platform": "robot", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 18593, + "classes": [ + "sugar_beet", + "weed" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy14102215", + "citation": "Bloch, V. (2024). *PaimioSB2023 dataset for sugar beet and weeds discrimination* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.10716274", + "bibtex": "@article{palva2024assessment,\n title={Assessment of the Performance of a Field Weeding Location-Based Robot Using YOLOv8},\n author={Palva, Reetta and Kaila, Eerikki and García-Pascual, Borja and Bloch, Victor},\n journal={Agronomy},\n volume={14},\n pages={2215},\n year={2024},\n publisher={MDPI}\n}", + "zip_size_bytes": 5714343217, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/sugar_beet_weed_detection", + "examples_image_url": "/img/agml/sample_images/sugar_beet_weed_detection_sample.webp" + }, + { + "name": "CCOD_cotton_flower_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Changji Laolonghe Experimental Base, Xinjiang Uygur Autonomous Region, China", + "Keshan Farm, Heilongjiang, China", + "Future Smart Pastoral, Luja, Kunshan, Suzhou, Jiangsu, China" + ], + "lat_lon": [ + "44.263, 87.332", + "48.033, 125.864", + "31.386, 120.976" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "cotton", + "potato", + "rice" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic 3 Multispectral RTK (M3M) quadcopter, 20 MP RGB camera, nadir, 12 m flight altitude, 85%/75% front/side overlap" + ], + "collection_period": "2024 (rice panicle images partly from the DRPD dataset, 2021-2022)", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 3986, + "classes": [ + "cotton_flower", + "potato_flower", + "cotton_boll", + "rice_panicle" + ], + "license": "gpl", + "documentation": "https://doi.org/10.3390/rs17050906", + "citation": "", + "bibtex": "@article{liu2025improved,\n title={Improved Detection and Location of Small Crop Organs by Fusing UAV Orthophoto Maps and Raw Images},\n author={Liu, Huaiyang and Li, Huibin and Wang, Haozhou and Liu, Chuanghai and Qian, Jianping and Wang, Zhanbiao and Geng, Changxing},\n journal={Remote Sensing},\n volume={17},\n pages={906},\n year={2025},\n publisher={MDPI}\n}", + "zip_size_bytes": 877608706, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/CCOD_cotton_flower_detection", + "examples_image_url": "/img/agml/sample_images/CCOD_cotton_flower_detection_sample.webp" + }, + { + "name": "maize_seedling_msdd_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Missouri Maize Center, University of Missouri, Columbia, Missouri, USA" + ], + "lat_lon": [], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "maize" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Phantom 4 (RGB video, 24/30 fps, nadir and oblique views, flown manually)", + "DJI Mavic 2 Pro (RGB video, 24/30 fps, nadir and oblique views, flown manually)" + ], + "collection_period": "2019-2022 growing seasons", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 3152, + "classes": [ + "single_seedling", + "double_seedling", + "triple_seedling" + ], + "license": "unknown", + "documentation": "https://doi.org/10.3390/agronomy16161605", + "citation": "", + "bibtex": "@article{kharismawati2026maize,\n title={Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN},\n author={Kharismawati, Dewi Endah and Kazic, Toni},\n journal={Agronomy},\n volume={16},\n pages={1605},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 15545624907, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/maize_seedling_msdd_detection", + "examples_image_url": "/img/agml/sample_images/maize_seedling_msdd_detection_sample.webp" + }, + { + "name": "UAV_SaSeg_spartina_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "weed_segmentation", + "location": [ + "Shaliu Subdistrict, Sanmen County, Taizhou, Zhejiang Province, China" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "Spartina alterniflora" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI multi-rotor UAV with RGB sensor and centimetre-level RTK module; orthomosaic at 0.02 m ground sample distance" + ], + "collection_period": "October 2025", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 358, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs18142375", + "citation": "Tang. (2026). *UAV-SaSeg* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.18697416", + "bibtex": "@article{chen2026fine,\n title={Fine-Scale Spartina alterniflora Mapping Using Advanced Deep Learning and High-Resolution UAV Imagery},\n author={Chen, Zegang and Tang, Yixing and Hu, Yulong and Chu, Bin and Long, Zhong and Xie, Dong and Zhang, Yunfei and Wang, Zhipan},\n journal={Remote Sensing},\n volume={18},\n pages={2375},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 668615529, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/UAV_SaSeg_spartina_segmentation", + "examples_image_url": "/img/agml/sample_images/UAV_SaSeg_spartina_segmentation_sample.webp" + }, + { + "name": "sorghum_uav_nitrogen_regression", + "machine_learning_task": "ground_truth", + "agricultural_task": "crop_phenotyping", + "location": [ + "International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, India" + ], + "lat_lon": [ + "17.53, 78.27" + ], + "country": "India", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "sorghum" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI ZENMUSE X5S RGB camera on a DJI Matrice 210 quadcopter, nadir view, 80-90% front/side overlap; plot images delineated from Agisoft Metashape orthomosaics (3-5 mm horizontal resolution)" + ], + "collection_period": "December 2021 - January 2022 (rabi 2021-2022) and December 2022 - January 2023 (rabi 2022-2023)", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "numericMeasurements", + "num_images": 470, + "classes": [], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture14101682", + "citation": "Choudhary, S., Patil, S., Anbazhagan, K., Chandramouli, M., Mamidi, S., KSVV, P., Baddam, R., Hammouch, H., El-Yacoubi, M. A., Masner, J., Vaněk, J., Qin, H., Michal, S., & Berbia, H. (2024). *RGB images of sorghum fields gathered by UAV flights* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.13733947", + "bibtex": "@article{hammouch2024hybrid,\n title={Hybrid-AI and Model Ensembling to Exploit UAV-Based RGB Imagery: An Evaluation of Sorghum Crop’s Nitrogen Content},\n author={Hammouch, Hajar and Patil, Suchitra and Choudhary, Sunita and El-Yacoubi, Mounim A. and Masner, Jan and Kholová, Jana and Anbazhagan, Krithika and Vaněk, Jiří and Qin, Huafeng and Stočes, Michal and Berbia, Hassan and Jagarlapudi, Adinarayana and Chandramouli, Magesh and Mamidi, Srinivas and Prasad, KVSV and Baddam, Rekha},\n journal={Agriculture},\n volume={14},\n pages={1682},\n year={2024},\n publisher={MDPI}\n}", + "zip_size_bytes": 1881107110, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/sorghum_uav_nitrogen_regression", + "examples_image_url": "/img/agml/sample_images/sorghum_uav_nitrogen_regression_sample.webp" + }, + { + "name": "apple_blotch_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "disease_detection", + "location": [ + "Julius Kühn Institute (JKI), Institute for Breeding Research on Fruit Crops, Dresden-Pillnitz, Saxony, Germany" + ], + "lat_lon": [], + "country": "Germany", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "apple" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon EOS 90D (2023) and Canon EOS 70D (2022) DSLR, 18-55 mm zoom lens at 45 mm, f/5.5, 1/250 s, ISO 125, tripod-mounted 30 cm above a Novoflex Magic Studio light table" + ], + "collection_period": "2022-2023", + "platform": "tripod", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 927, + "classes": [ + "apple_blotch_acervulus" + ], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/agronomy14051042", + "citation": "", + "bibtex": "@article{reim2024yolo,\n title={YOLO-Based Phenotyping of Apple Blotch Disease (Diplocarpon coronariae) in Genetic Resources after Artificial Inoculation},\n author={Reim, Stefanie and Richter, Sophie and Leonhardt, Oskar and Maß, Virginia and Wöhner, Thomas Wolfgang},\n journal={Agronomy},\n volume={14},\n pages={1042},\n year={2024},\n publisher={MDPI}\n}", + "zip_size_bytes": 123535822, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/apple_blotch_detection", + "examples_image_url": "/img/agml/sample_images/apple_blotch_detection_sample.webp" + }, + { + "name": "niwo_tree_crown_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "", + "location": [ + "Niwot Ridge (NEON site NIWO), Colorado, USA" + ], + "lat_lon": [ + "40.05425, -105.58237" + ], + "country": "USA", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "lodgepole pine", + "subalpine fir", + "Engelmann spruce" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "NEON Airborne Observation Platform (AOP) aircraft RGB camera, 0.1 m ground resolution orthomosaic tiles" + ], + "collection_period": "2018", + "platform": "aerial", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 88, + "classes": [ + "tree" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/rs16234365", + "citation": "Harmon, I. (2024). *NEON Neuro-Symbolic Dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.14194555", + "bibtex": "@article{harmon2024neuro,\n title={A Neuro-Symbolic Framework for Tree Crown Delineation and Tree Species Classification},\n author={Harmon, Ira and Weinstein, Ben and Bohlman, Stephanie and White, Ethan and Wang, Daisy Zhe},\n journal={Remote Sensing},\n volume={16},\n pages={4365},\n year={2024},\n publisher={MDPI}\n}", + "zip_size_bytes": 31376870, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/niwo_tree_crown_detection", + "examples_image_url": "/img/agml/sample_images/niwo_tree_crown_detection_sample.webp" + }, + { + "name": "appleleaf9_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [], + "lat_lon": [], + "country": "", + "environment": "mixed", + "real_or_synthetic": "real", + "crop_types": [ + "apple" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Compiled from four public datasets captured with various cameras and phones (PlantVillage, ATLDSD, Plant Pathology 2020 and 2021)" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 14582, + "classes": [ + "Alternaria leaf spot", + "Brown spot", + "Frogeye leaf spot", + "Grey spot", + "Health", + "Mosaic", + "Powdery mildew", + "Rust", + "Scab" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agronomy12112784", + "citation": "", + "bibtex": "@article{yang2022efficient,\n title={Efficient Identification of Apple Leaf Diseases in the Wild Using Convolutional Neural Networks},\n author={Yang, Qing and Duan, Shukai and Wang, Lidan},\n journal={Agronomy},\n volume={12},\n pages={2784},\n year={2022},\n publisher={MDPI}\n}", + "zip_size_bytes": 1184629808, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/appleleaf9_disease_classification", + "examples_image_url": "/img/agml/sample_images/appleleaf9_disease_classification_sample.webp" + }, + { + "name": "pdsnet_field_plant_disease_segmentation", + "machine_learning_task": "semantic_segmentation", + "agricultural_task": "disease_segmentation", + "location": [ + "Jiaozuo Academy of Agriculture and Forestry Sciences, Jiaozuo, Henan, China (wheat stripe rust); soybean and cedar apple rust images from public sources" + ], + "lat_lon": [], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "apple", + "soybean", + "wheat" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Various cameras (field-collected and public images); padded to square and resized to 640 x 640" + ], + "collection_period": "May 2023 (wheat stripe rust)", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "segmentationMask", + "num_images": 240, + "classes": [], + "license": "cc-by-nc-4.0", + "documentation": "https://doi.org/10.3390/agriculture15060583", + "citation": "", + "bibtex": "@article{zhao2025plant,\n title={Plant Disease Segmentation Networks for Fast Automatic Severity Estimation Under Natural Field Scenarios},\n author={Zhao, Chenyi and Li, Changchun and Wang, Xin and Wu, Xifang and Du, Yongquan and Chai, Huabin and Cai, Taiyi and Xiang, Hengmao and Jiao, Yinghua},\n journal={Agriculture},\n volume={15},\n pages={583},\n year={2025},\n publisher={MDPI}\n}", + "zip_size_bytes": 11327467, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/pdsnet_field_plant_disease_segmentation", + "examples_image_url": "/img/agml/sample_images/pdsnet_field_plant_disease_segmentation_sample.webp" + }, + { + "name": "tomato_detection_kaggle", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "tomato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 895, + "classes": [ + "tomato" + ], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/agronomy15071593", + "citation": "", + "bibtex": "@article{wang2025swmd,\n title={SWMD-YOLO: A Lightweight Model for Tomato Detection in Greenhouse Environments},\n author={Wang, Quan and Hua, Ye and Lou, Qiongdan and Kan, Xi},\n journal={Agronomy},\n volume={15},\n pages={1593},\n year={2025},\n publisher={MDPI}\n}", + "zip_size_bytes": 188678632, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/tomato_detection_kaggle", + "examples_image_url": "/img/agml/sample_images/tomato_detection_kaggle_sample.webp" + }, + { + "name": "mtl_plotcounter_soybean_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "crop_detection", + "location": [ + "Soybean breeding field, Jinzhong, Shanxi Province, China" + ], + "lat_lon": [ + "37.434, 112.594" + ], + "country": "China", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "soybean" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "DJI Mavic 2 Pro UAV with Hasselblad L1D-20C camera, 5 m altitude, 2 m/s, 0.1 cm/pixel GSD" + ], + "collection_period": "2023", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 54, + "classes": [ + "soybean_seedling" + ], + "license": "apache-2.0", + "documentation": "https://doi.org/10.3390/rs17152688", + "citation": "", + "bibtex": "@article{xue2025mtl,\n title={MTL-PlotCounter: Multitask Driven Soybean Seedling Counting at the Plot Scale Based on UAV Imagery},\n author={Xue, Xiaoqin and Li, Chenfei and Liu, Zonglin and Sun, Yile and Li, Xuru and Song, Haiyan},\n journal={Remote Sensing},\n volume={17},\n pages={2688},\n year={2025},\n publisher={MDPI}\n}", + "zip_size_bytes": 137411141, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/mtl_plotcounter_soybean_detection", + "examples_image_url": "/img/agml/sample_images/mtl_plotcounter_soybean_detection_sample.webp" + }, + { + "name": "wheat_seed_variety_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "University of Agriculture, Faisalabad, Punjab, Pakistan" + ], + "lat_lon": [ + "31.438425, 73.074146" + ], + "country": "Pakistan", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "wheat" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Canon EOS 5D Mark III DSLR; images resized to 256 x 256" + ], + "collection_period": "2024", + "platform": "fixed", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 1124, + "classes": [ + "Akbar-19", + "Dilkash-20", + "Urooj-22" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.111690", + "citation": "paal lab, precision lab. (2025). *Varietal purity of wheat seeds dataset* [Dataset]. Mendeley Data. https://doi.org/10.17632/W5248V9FK3.1", + "bibtex": "@article{nawaz2025high,\n title={High-resolution RGB image dataset for wheat seed varietal identification and purity assessment},\n author={Nawaz, Mehreen and Safder, Sadaf and Riaz, Shazia and Ali, Saqib},\n journal={Data in Brief},\n volume={61},\n pages={111690},\n year={2025},\n publisher={Elsevier}\n}", + "zip_size_bytes": 31122041, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/wheat_seed_variety_classification", + "examples_image_url": "/img/agml/sample_images/wheat_seed_variety_classification_sample.webp" + }, + { + "name": "medicinal_leaf_species_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "crop_classification", + "location": [ + "Diploma Krishibid Nursery and Hossain Nursery, Dattapara, Birulia, Savar, Dhaka, Bangladesh" + ], + "lat_lon": [ + "23.755, 90.376" + ], + "country": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "arjun (Terminalia arjuna)", + "curry leaf (Murraya koenigii)", + "marsh pennywort (Hydrocotyle vulgaris)", + "mint (Mentha spp.)", + "neem (Azadirachta indica)", + "Kalanchoe pinnata" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Apple iPhone 13 Pro camera, natural light; images resized to 512 x 512" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 1380, + "augmented_num_images": 9660, + "augmented_zip_size_bytes": 134717043, + "classes": [ + "Arjun Leaf", + "Curry Leaf", + "Marsh Pennywort Leaf", + "Mint Leaf", + "Neem Leaf", + "Rubble Leaf" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.112129", + "citation": "Mafi, M. M. H. M. (2023). *Medicinal Plant Identification Dataset* [Dataset]. Mendeley Data. https://doi.org/10.17632/FJ93RRFV2Y.2", + "bibtex": "@article{matin2025extensive,\n title={An extensive visual data for reliable identification of medicinal plant leaves},\n author={Matin, Md. Mafiul Hasan and Sefatullah, Md. and Kabir, Md Shariar and Habib, Haifa Binte},\n journal={Data in Brief},\n volume={63},\n pages={112129},\n year={2025},\n publisher={Elsevier}\n}", + "zip_size_bytes": 217248961, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/medicinal_leaf_species_classification", + "examples_image_url": "/img/agml/sample_images/medicinal_leaf_species_classification_sample.webp" + }, + { + "name": "roseleafset_disease_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Shumi Nursery, Shetu Nursery, Bismillah Nursery and other rose gardens, Amin Model Town, Khagan, Ashulia, Savar, Dhaka, Bangladesh" + ], + "lat_lon": [ + "23.884, 90.324" + ], + "country": "Bangladesh", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "rose" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Vivo IQOO Z9x smartphone camera, 3264 x 2448 px JPEG, handheld" + ], + "collection_period": "October 30 - November 6, 2024", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 3113, + "augmented_num_images": 10000, + "augmented_zip_size_bytes": 400892951, + "classes": [ + "Black Spot", + "Dry Leaf", + "Healthy Leaf", + "Leaf Holes" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1016/j.dib.2025.112108", + "citation": "Jinia, J. T., Rahman, M. S., Mojumdar, M. U., & Bijoy, M. H. I. (2025). *RoseLeafSet: Real-World Leaf Image Dataset for AI-Based Agricultural Solutions* [Dataset]. Mendeley Data. https://doi.org/10.17632/9G668BFHY5.3", + "bibtex": "", + "zip_size_bytes": 5570992551, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/roseleafset_disease_classification", + "examples_image_url": "/img/agml/sample_images/roseleafset_disease_classification_sample.webp" + }, + { + "name": "medicinal_plant_parts_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "plant_classification", + "location": [ + "Ethiopia" + ], + "lat_lon": [], + "country": "Ethiopia", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "medicinal plants" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "Samsung A30s smartphone (25 MP)", + "Tecno Spark K7 smartphone (13 MP)" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 15100, + "classes": [ + "leaf", + "seed", + "root", + "bark", + "root_leaf", + "leaf_seed", + "bark_leaf", + "un_identified" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.1371/journal.pone.0322936", + "citation": "Aguate, M. (2024). *Medicinal Plant leaf images* [Dataset]. figshare. https://doi.org/10.6084/M9.FIGSHARE.26426806.V1", + "bibtex": "@article{widneh2025identification,\n title={Identification of medicinal plant parts using depth-wise separable convolutional neural network},\n author={Widneh, Misganaw Aguate and Alemu, Amlakie Aschale},\n journal={PLOS One},\n volume={20},\n pages={e0322936},\n year={2025},\n publisher={Public Library of Science}\n}", + "zip_size_bytes": 88211394, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/medicinal_plant_parts_classification", + "examples_image_url": "/img/agml/sample_images/medicinal_plant_parts_classification_sample.webp" + }, + { + "name": "sesame_crop_weed_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "weed_detection", + "location": [], + "lat_lon": [], + "country": "", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "sesame" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "camera (4000 x 3000 originals)" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 1300, + "classes": [ + "sesame", + "weed" + ], + "license": "cc0-1.0", + "documentation": "https://doi.org/10.3390/agriculture16101067", + "citation": "", + "bibtex": "@article{zhu2026d2fnet,\n title={D2FNet: A Lightweight Dual-Driven Texture–Semantic Fusion Network for Fine-Grained Real-Time UAV Weed–Crop Detection},\n author={Zhu, Chenghua and Wu, Siyan and Chao, Xingqi and Lao, Minxuan and Yang, Lei and Cai, Lihua},\n journal={Agriculture},\n volume={16},\n pages={1067},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 82798437, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/sesame_crop_weed_detection", + "examples_image_url": "/img/agml/sample_images/sesame_crop_weed_detection_sample.webp" + }, + { + "name": "larch_casebearer_uav_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "forest_damage_detection", + "location": [ + "Bebehojd, Ekbacka, Jallasvag, Kampe and Nordkap, Vastergotland, Sweden" + ], + "lat_lon": [], + "country": "Sweden", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "larch" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "UAV RGB camera" + ], + "collection_period": "May 27 and August 19, 2019", + "platform": "uav", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 835, + "classes": [ + "healthy_larch", + "light_damage", + "high_damage", + "other_tree", + "unknown" + ], + "license": "cdla-permissive-1.0", + "documentation": "https://doi.org/10.3390/rs18172916", + "citation": "", + "bibtex": "@article{wang2026dromal,\n title={DROMAL-Net: A Forest Larch Casebearer Detection Model Focusing on Global Feature Enhancement and Positional Dynamic Clustering},\n author={Wang, Jiaxuan and Liu, Lu and Tang, Yuyang and Ma, Tao and Song, Xiangyu and Qiao, Pan and Ding, Zhen},\n journal={Remote Sensing},\n volume={18},\n pages={2916},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 1920915230, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/larch_casebearer_uav_detection", + "examples_image_url": "/img/agml/sample_images/larch_casebearer_uav_detection_sample.webp" + }, + { + "name": "tomato_adaptive_yolov8_detection", + "machine_learning_task": "object_detection", + "agricultural_task": "fruit_maturity_detection", + "location": [ + "Gazipur, Bangladesh" + ], + "lat_lon": [ + "24.00, 90.42" + ], + "country": "Bangladesh", + "environment": "field", + "real_or_synthetic": "real", + "crop_types": [ + "tomato" + ], + "sensor_modality": "rgb", + "imaging_equipment": [], + "collection_period": "2026", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "boundingBox", + "num_images": 604, + "classes": [ + "full_ripened", + "half_ripened", + "green" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture16101130", + "citation": "Rahim, U. F., & Rahman, M. M. (2026). *OpenField-BD-Tomato-Maturity-Detection Dataset* [Dataset]. Zenodo. https://doi.org/10.5281/ZENODO.20176021", + "bibtex": "@article{rahim2026tomato,\n title={Tomato-Adaptive Attention YOLOv8 for Accurate and Interpretable Maturity Detection Across Diverse Environments},\n author={Rahim, Umme Fawzia and Rahman, Md. Mushibur and Mineno, Hiroshi},\n journal={Agriculture},\n volume={16},\n pages={1130},\n year={2026},\n publisher={MDPI}\n}", + "zip_size_bytes": 2396937281, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/tomato_adaptive_yolov8_detection", + "examples_image_url": "/img/agml/sample_images/tomato_adaptive_yolov8_detection_sample.webp" + }, + { + "name": "uci_rice_leaf_diseases_classification", + "machine_learning_task": "image_classification", + "agricultural_task": "disease_classification", + "location": [ + "Gujarat, India (authors' region; collection sites not stated)" + ], + "lat_lon": [], + "country": "India", + "environment": "lab", + "real_or_synthetic": "real", + "crop_types": [ + "rice" + ], + "sensor_modality": "rgb", + "imaging_equipment": [ + "digital camera" + ], + "collection_period": "", + "platform": "handheld", + "input_data_format": "parquet", + "annotation_format": "classLabel", + "num_images": 120, + "classes": [ + "bacterial_leaf_blight", + "brown_spot", + "leaf_smut" + ], + "license": "cc-by-4.0", + "documentation": "https://doi.org/10.3390/agriculture13081549", + "citation": "", + "bibtex": "@article{Prajapati_2017, title={Detection and classification of rice plant diseases}, volume={11}, ISSN={1875-8843}, url={http://dx.doi.org/10.3233/IDT-170301}, DOI={10.3233/idt-170301}, number={3}, journal={Intelligent Decision Technologies}, publisher={SAGE Publications}, author={Prajapati, Harshadkumar B. and Shah, Jitesh P. and Dabhi, Vipul K.}, year={2017}, month=Aug, pages={357--373} }", + "zip_size_bytes": 171414455, + "source": "huggingface", + "hf_link": "https://huggingface.co/datasets/Project-AgML/uci_rice_leaf_diseases_classification", + "examples_image_url": "/img/agml/sample_images/uci_rice_leaf_diseases_classification_sample.webp" } ] diff --git a/static/img/agml/sample_images/AppleTreeDataset_intertwined_segmentation_sample.webp b/static/img/agml/sample_images/AppleTreeDataset_intertwined_segmentation_sample.webp new file mode 100644 index 0000000..5c05262 Binary files /dev/null and b/static/img/agml/sample_images/AppleTreeDataset_intertwined_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/CCOD_cotton_flower_detection_sample.webp b/static/img/agml/sample_images/CCOD_cotton_flower_detection_sample.webp new file mode 100644 index 0000000..1eb2bb8 Binary files /dev/null and b/static/img/agml/sample_images/CCOD_cotton_flower_detection_sample.webp differ diff --git a/static/img/agml/sample_images/CT_PestNet_crop_pest_classification_sample.webp b/static/img/agml/sample_images/CT_PestNet_crop_pest_classification_sample.webp new file mode 100644 index 0000000..6ee02b5 Binary files /dev/null and b/static/img/agml/sample_images/CT_PestNet_crop_pest_classification_sample.webp differ diff --git a/static/img/agml/sample_images/CosegPP_Buckwheat_Sunflower_segmentation_sample.webp b/static/img/agml/sample_images/CosegPP_Buckwheat_Sunflower_segmentation_sample.webp new file mode 100644 index 0000000..b8f2cd4 Binary files /dev/null and b/static/img/agml/sample_images/CosegPP_Buckwheat_Sunflower_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/GOPD_YOLO_scallion_posture_detection_sample.webp b/static/img/agml/sample_images/GOPD_YOLO_scallion_posture_detection_sample.webp new file mode 100644 index 0000000..851e1eb Binary files /dev/null and b/static/img/agml/sample_images/GOPD_YOLO_scallion_posture_detection_sample.webp differ diff --git a/static/img/agml/sample_images/Greenhouse_Tomato_AgRob_object_detection_sample.webp b/static/img/agml/sample_images/Greenhouse_Tomato_AgRob_object_detection_sample.webp new file mode 100644 index 0000000..1a3e823 Binary files /dev/null and b/static/img/agml/sample_images/Greenhouse_Tomato_AgRob_object_detection_sample.webp differ diff --git a/static/img/agml/sample_images/LeafNet_plant_disease_classification_sample.webp b/static/img/agml/sample_images/LeafNet_plant_disease_classification_sample.webp new file mode 100644 index 0000000..0bb456b Binary files /dev/null and b/static/img/agml/sample_images/LeafNet_plant_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/OccPepSeg_YOLO_occluded_segmentation_sample.webp b/static/img/agml/sample_images/OccPepSeg_YOLO_occluded_segmentation_sample.webp new file mode 100644 index 0000000..9b76ee2 Binary files /dev/null and b/static/img/agml/sample_images/OccPepSeg_YOLO_occluded_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/Plant_leaf_diseases_dataset_classification_sample.webp b/static/img/agml/sample_images/Plant_leaf_diseases_dataset_classification_sample.webp new file mode 100644 index 0000000..dbc825f Binary files /dev/null and b/static/img/agml/sample_images/Plant_leaf_diseases_dataset_classification_sample.webp differ diff --git a/static/img/agml/sample_images/UAV_SaSeg_spartina_segmentation_sample.webp b/static/img/agml/sample_images/UAV_SaSeg_spartina_segmentation_sample.webp new file mode 100644 index 0000000..e61ea91 Binary files /dev/null and b/static/img/agml/sample_images/UAV_SaSeg_spartina_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/YOLOv8_PlantDisease_classification_sample.webp b/static/img/agml/sample_images/YOLOv8_PlantDisease_classification_sample.webp new file mode 100644 index 0000000..29ca5a8 Binary files /dev/null and b/static/img/agml/sample_images/YOLOv8_PlantDisease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/apple_blotch_detection_sample.webp b/static/img/agml/sample_images/apple_blotch_detection_sample.webp new file mode 100644 index 0000000..2aac489 Binary files /dev/null and b/static/img/agml/sample_images/apple_blotch_detection_sample.webp differ diff --git a/static/img/agml/sample_images/appleleaf9_disease_classification_sample.webp b/static/img/agml/sample_images/appleleaf9_disease_classification_sample.webp new file mode 100644 index 0000000..5a6778f Binary files /dev/null and b/static/img/agml/sample_images/appleleaf9_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_2_sample.webp b/static/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_2_sample.webp new file mode 100644 index 0000000..95698ac Binary files /dev/null and b/static/img/agml/sample_images/cocoa_pod_disease_gh_image_classification_2_sample.webp differ diff --git a/static/img/agml/sample_images/grapevine_bunch_condition_detection_sample.webp b/static/img/agml/sample_images/grapevine_bunch_condition_detection_sample.webp new file mode 100644 index 0000000..f5094e5 Binary files /dev/null and b/static/img/agml/sample_images/grapevine_bunch_condition_detection_sample.webp differ diff --git a/static/img/agml/sample_images/grapevine_bunch_detection_sample.webp b/static/img/agml/sample_images/grapevine_bunch_detection_sample.webp new file mode 100644 index 0000000..e14e374 Binary files /dev/null and b/static/img/agml/sample_images/grapevine_bunch_detection_sample.webp differ diff --git a/static/img/agml/sample_images/larch_casebearer_uav_detection_sample.webp b/static/img/agml/sample_images/larch_casebearer_uav_detection_sample.webp new file mode 100644 index 0000000..16df997 Binary files /dev/null and b/static/img/agml/sample_images/larch_casebearer_uav_detection_sample.webp differ diff --git a/static/img/agml/sample_images/maize_seedling_msdd_detection_sample.webp b/static/img/agml/sample_images/maize_seedling_msdd_detection_sample.webp new file mode 100644 index 0000000..0a5d9cc Binary files /dev/null and b/static/img/agml/sample_images/maize_seedling_msdd_detection_sample.webp differ diff --git a/static/img/agml/sample_images/mango_fruit_stem_detection_sample.webp b/static/img/agml/sample_images/mango_fruit_stem_detection_sample.webp new file mode 100644 index 0000000..a58bde1 Binary files /dev/null and b/static/img/agml/sample_images/mango_fruit_stem_detection_sample.webp differ diff --git a/static/img/agml/sample_images/mango_pest_original_classification_sample.webp b/static/img/agml/sample_images/mango_pest_original_classification_sample.webp new file mode 100644 index 0000000..dc3ed4e Binary files /dev/null and b/static/img/agml/sample_images/mango_pest_original_classification_sample.webp differ diff --git a/static/img/agml/sample_images/medicinal_leaf_species_classification_sample.webp b/static/img/agml/sample_images/medicinal_leaf_species_classification_sample.webp new file mode 100644 index 0000000..d086279 Binary files /dev/null and b/static/img/agml/sample_images/medicinal_leaf_species_classification_sample.webp differ diff --git a/static/img/agml/sample_images/medicinal_plant_parts_classification_sample.webp b/static/img/agml/sample_images/medicinal_plant_parts_classification_sample.webp new file mode 100644 index 0000000..ddb142f Binary files /dev/null and b/static/img/agml/sample_images/medicinal_plant_parts_classification_sample.webp differ diff --git a/static/img/agml/sample_images/mtl_plotcounter_soybean_detection_sample.webp b/static/img/agml/sample_images/mtl_plotcounter_soybean_detection_sample.webp new file mode 100644 index 0000000..5dbadb4 Binary files /dev/null and b/static/img/agml/sample_images/mtl_plotcounter_soybean_detection_sample.webp differ diff --git a/static/img/agml/sample_images/niwo_tree_crown_detection_sample.webp b/static/img/agml/sample_images/niwo_tree_crown_detection_sample.webp new file mode 100644 index 0000000..b040a4d Binary files /dev/null and b/static/img/agml/sample_images/niwo_tree_crown_detection_sample.webp differ diff --git a/static/img/agml/sample_images/paddy_weed_target_detection_sample.webp b/static/img/agml/sample_images/paddy_weed_target_detection_sample.webp new file mode 100644 index 0000000..4099856 Binary files /dev/null and b/static/img/agml/sample_images/paddy_weed_target_detection_sample.webp differ diff --git a/static/img/agml/sample_images/pdsnet_field_plant_disease_segmentation_sample.webp b/static/img/agml/sample_images/pdsnet_field_plant_disease_segmentation_sample.webp new file mode 100644 index 0000000..028fa8f Binary files /dev/null and b/static/img/agml/sample_images/pdsnet_field_plant_disease_segmentation_sample.webp differ diff --git a/static/img/agml/sample_images/pea_seed_viability_classification_sample.webp b/static/img/agml/sample_images/pea_seed_viability_classification_sample.webp new file mode 100644 index 0000000..77ac655 Binary files /dev/null and b/static/img/agml/sample_images/pea_seed_viability_classification_sample.webp differ diff --git a/static/img/agml/sample_images/potato_disease_classification_sample.webp b/static/img/agml/sample_images/potato_disease_classification_sample.webp new file mode 100644 index 0000000..17deaae Binary files /dev/null and b/static/img/agml/sample_images/potato_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/potato_leaf_disease_classification_2_sample.webp b/static/img/agml/sample_images/potato_leaf_disease_classification_2_sample.webp new file mode 100644 index 0000000..3c1bec9 Binary files /dev/null and b/static/img/agml/sample_images/potato_leaf_disease_classification_2_sample.webp differ diff --git a/static/img/agml/sample_images/roseleafset_disease_classification_sample.webp b/static/img/agml/sample_images/roseleafset_disease_classification_sample.webp new file mode 100644 index 0000000..e0b181e Binary files /dev/null and b/static/img/agml/sample_images/roseleafset_disease_classification_sample.webp differ diff --git a/static/img/agml/sample_images/sesame_crop_weed_detection_sample.webp b/static/img/agml/sample_images/sesame_crop_weed_detection_sample.webp new file mode 100644 index 0000000..ab8c767 Binary files /dev/null and b/static/img/agml/sample_images/sesame_crop_weed_detection_sample.webp differ diff --git a/static/img/agml/sample_images/sorghum_uav_nitrogen_regression_sample.webp b/static/img/agml/sample_images/sorghum_uav_nitrogen_regression_sample.webp new file mode 100644 index 0000000..ec7487a Binary files /dev/null and b/static/img/agml/sample_images/sorghum_uav_nitrogen_regression_sample.webp differ diff --git a/static/img/agml/sample_images/sugar_beet_weed_detection_sample.webp b/static/img/agml/sample_images/sugar_beet_weed_detection_sample.webp new file mode 100644 index 0000000..ae49101 Binary files /dev/null and b/static/img/agml/sample_images/sugar_beet_weed_detection_sample.webp differ diff --git a/static/img/agml/sample_images/tomato_adaptive_yolov8_detection_sample.webp b/static/img/agml/sample_images/tomato_adaptive_yolov8_detection_sample.webp new file mode 100644 index 0000000..0bc7d33 Binary files /dev/null and b/static/img/agml/sample_images/tomato_adaptive_yolov8_detection_sample.webp differ diff --git a/static/img/agml/sample_images/tomato_detection_kaggle_sample.webp b/static/img/agml/sample_images/tomato_detection_kaggle_sample.webp new file mode 100644 index 0000000..30ea814 Binary files /dev/null and b/static/img/agml/sample_images/tomato_detection_kaggle_sample.webp differ diff --git a/static/img/agml/sample_images/uci_rice_leaf_diseases_classification_sample.webp b/static/img/agml/sample_images/uci_rice_leaf_diseases_classification_sample.webp new file mode 100644 index 0000000..547877d Binary files /dev/null and b/static/img/agml/sample_images/uci_rice_leaf_diseases_classification_sample.webp differ diff --git a/static/img/agml/sample_images/wetveg2mm_riparian_vegetation_classification_sample.webp b/static/img/agml/sample_images/wetveg2mm_riparian_vegetation_classification_sample.webp new file mode 100644 index 0000000..a957d1f Binary files /dev/null and b/static/img/agml/sample_images/wetveg2mm_riparian_vegetation_classification_sample.webp differ diff --git a/static/img/agml/sample_images/wheat_seed_variety_classification_sample.webp b/static/img/agml/sample_images/wheat_seed_variety_classification_sample.webp new file mode 100644 index 0000000..391bf32 Binary files /dev/null and b/static/img/agml/sample_images/wheat_seed_variety_classification_sample.webp differ diff --git a/static/img/agml/sample_images/ws_dino_weed_segmentation_sample.webp b/static/img/agml/sample_images/ws_dino_weed_segmentation_sample.webp new file mode 100644 index 0000000..35a677e Binary files /dev/null and b/static/img/agml/sample_images/ws_dino_weed_segmentation_sample.webp differ